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Journal articles on the topic 'Low-distortion embedding'
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1
Castermans, Thom, Kevin Verbeek, Bettina Speckmann, Michel A. Westenberg, Rob Koopman, Shenghui Wang, Hein van den Berg, and Arianna Betti. "SolarView: Low Distortion Radial Embedding with a Focus." IEEE Transactions on Visualization and Computer Graphics 25, no. 10 (October 1, 2019): 2969–82. http://dx.doi.org/10.1109/tvcg.2018.2865361.
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Shih, Frank Y., and Xin Zhong. "Intelligent Watermarking for High-Capacity Low-Distortion Data Embedding." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 05 (April 21, 2016): 1654003. http://dx.doi.org/10.1142/s0218001416540033.
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Image watermarking intends to hide secret data for the purposes of copyright protection, image authentication, data privacy, and broadcast monitoring. The ultimate goal is to achieve highest embedding capacity and lowest image distortion. In this paper, we present an intelligent watermarking scheme which can automatically analyze an image content to extract significant regions of interest (ROIs). A ROI is an area involving crucial information, and will be kept intact. The remaining regions of non-interest (RONIs) are collated for embedding watermarks, and will be ranked according to their entropy fuzzy memberships into a degree of importance. They are embedded by different amounts of bits to achieve optimal watermarking. The watermark is compressed and embedded in the frequency domain of the image. Experimental results show that the proposed technique has accomplished high capacity, high robustness, and high PSNR (peak signal-to-noise ratio) watermarking.
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Li, Wan Qi, Heng Wang, Che Nian, Huang Wei, and Hong Yao You. "Minimizing the Embedding Impact Using Network Flow Algorithms." Advanced Materials Research 341-342 (September 2011): 478–83. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.478.
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A novel method of minimizing the embedding impact is proposed in this paper. Optimal embedding is achieved using network flow algorithms by considering the modifications on the cover image as flows of pixels among different states. This method is not an independent steganographic scheme, but rather it minimizes the embedding impact after the embedding process and it’s compatible with the majority of embedding techniques. Due to its dependence on the embedding process, many optimization problems, such as the minimization of a globally interactive distortion function, that are intractable during the embedding process can be solved with relatively low computational cost by rectifying the modifications on the cover image after the embedding process. A distortion function based on Kullback-Leibler divergence is provided as a concrete example to illustrate the basic idea of this method.
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Nguyen, Dinh-Chien, Thai-Son Nguyen, Chin-Chen Chang, Huan-Sheng Hsueh, and Fang-Rong Hsu. "High Embedding Capacity Data Hiding Algorithm for H.264/AVC Video Sequences without Intraframe Distortion Drift." Security and Communication Networks 2018 (August 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/2029869.
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Data hiding is a technique that allows secret data to be delivered securely by embedding the data into cover digital media. In this paper, we propose a new data hiding algorithm for H.264/advanced video coding (AVC) of video sequences with high embedding capacity. In the proposed scheme, to embed secret data into the quantized discrete cosine transform (QDCT) coefficients of I frames without any intraframe distortion drift, some embeddable coefficient pairs are selected in each block, and they are divided into two different groups, i.e., the embedding group and the averting group. The embedding group is used to carry the secret data, and the averting group is used to prevent distortion drift in the adjacent blocks. The experimental results show that the proposed scheme can avoid intraframe distortion drift and guarantee low distortion of video sequences. In addition, the proposed scheme provides enhanced embedding capacity compared to previous schemes. Moreover, the embedded secret data can be extracted completely without the requirement of the original secret data.
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Hu, Yongjin, Xiyan Li, and Jun Ma. "A Novel LSB Matching Algorithm Based on Information Pre-Processing." Mathematics 10, no. 1 (December 21, 2021): 8. http://dx.doi.org/10.3390/math10010008.
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This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes.
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Wang, Ruosong, and David P. Woodruff. "Tight Bounds for ℓ 1 Oblivious Subspace Embeddings." ACM Transactions on Algorithms 18, no. 1 (January 31, 2022): 1–32. http://dx.doi.org/10.1145/3477537.
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An \ell _p oblivious subspace embedding is a distribution over r \times n matrices \Pi such that for any fixed n \times d matrix A , \[ \Pr _{\Pi }[\textrm {for all }x, \ \Vert Ax\Vert _p \le \Vert \Pi Ax\Vert _p \le \kappa \Vert Ax\Vert _p] \ge 9/10, \] where r is the dimension of the embedding, \kappa is the distortion of the embedding, and for an n -dimensional vector y , \Vert y\Vert _p = (\sum _{i=1}^n |y_i|^p)^{1/p} is the \ell _p -norm. Another important property is the sparsity of \Pi , that is, the maximum number of non-zero entries per column, as this determines the running time of computing \Pi A . While for p = 2 there are nearly optimal tradeoffs in terms of the dimension, distortion, and sparsity, for the important case of 1 \le p \lt 2 , much less was known. In this article, we obtain nearly optimal tradeoffs for \ell _1 oblivious subspace embeddings, as well as new tradeoffs for 1 \lt p \lt 2 . Our main results are as follows: (1) We show for every 1 \le p \lt 2 , any oblivious subspace embedding with dimension r has distortion \[ \kappa = \Omega \left(\frac{1}{\left(\frac{1}{d}\right)^{1 / p} \log ^{2 / p}r + \left(\frac{r}{n}\right)^{1 / p - 1 / 2}}\right). \] When r = {\operatorname{poly}}(d) \ll n in applications, this gives a \kappa = \Omega (d^{1/p}\log ^{-2/p} d) lower bound, and shows the oblivious subspace embedding of Sohler and Woodruff (STOC, 2011) for p = 1 is optimal up to {\operatorname{poly}}(\log (d)) factors. (2) We give sparse oblivious subspace embeddings for every 1 \le p \lt 2 . Importantly, for p = 1 , we achieve r = O(d \log d) , \kappa = O(d \log d) and s = O(\log d) non-zero entries per column. The best previous construction with s \le {\operatorname{poly}}(\log d) is due to Woodruff and Zhang (COLT, 2013), giving \kappa = \Omega (d^2 {\operatorname{poly}}(\log d)) or \kappa = \Omega (d^{3/2} \sqrt {\log n} \cdot {\operatorname{poly}}(\log d)) and r \ge d \cdot {\operatorname{poly}}(\log d) ; in contrast our r = O(d \log d) and \kappa = O(d \log d) are optimal up to {\operatorname{poly}}(\log (d)) factors even for dense matrices. We also give (1) \ell _p oblivious subspace embeddings with an expected 1+\varepsilon number of non-zero entries per column for arbitrarily small \varepsilon \gt 0 , and (2) the first oblivious subspace embeddings for 1 \le p \lt 2 with O(1) -distortion and dimension independent of n . Oblivious subspace embeddings are crucial for distributed and streaming environments, as well as entrywise \ell _p low-rank approximation. Our results give improved algorithms for these applications.
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Zhang, Shun, Liang Yang, Xihao Xu, and Tiegang Gao. "Secure Steganography in JPEG Images Based on Histogram Modification and Hyper Chaotic System." International Journal of Digital Crime and Forensics 10, no. 1 (January 2018): 40–53. http://dx.doi.org/10.4018/ijdcf.2018010104.
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Security always plays an important role in the communication. Steganography, which conceals the process of communication, is another efficient way to achieve secure communication besides encryption. This paper proposes a secure steganography scheme in JPEG images with high embedding capacity and low distortion to the cover image. It embeds the additional information by modifying the DCT coefficients in JPEG images. Considering the size of the additional information, some DCT coefficients are adaptively selected in the embedding process. Two chaotic encryption strategies are designed based on the hyper-chaotic system to encrypt the additional information before the embedding to enhance the security. Extensive experiments have demonstrated the validity and efficiency of this proposed scheme. Compared with some existing schemes, it offers larger embedding rate and lower distortion with stronger security.
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Yang, Yuhang, Xuyu Xiang, Jiaohua Qin, Yun Tan, Zhangdong Wang, and Yajie Liu. "High-Embedded Low-Distortion Multihistogram Shift Video Reversible Data Hiding Based on DCT Coefficient." Electronics 12, no. 7 (March 31, 2023): 1652. http://dx.doi.org/10.3390/electronics12071652.
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Video reversible data hiding technology can be applied to copyright protection, medical images, the military, and other fields, but it cannot guarantee high visual quality with an effective embedded capacity. In this paper, a high-embedding and low-distortion reversible data hiding scheme based on a discrete cosine transform (DCT) coefficients method is proposed. The scheme first decodes the original video stream with entropy, obtains all the DCT blocks, and selects the embeddable DCT blocks according to the capacity of the zero factor. Then, it divides the coefficients in the DCT blocks into the shift and embedding coefficients. The shift coefficients directly generate a one-dimensional histogram; the embedding coefficients generate a two-dimensional histogram according to paired strategies. Finally, the secret data can be successfully embedded according to the proposed two-dimensional histogram shift reversible data hiding scheme. This scheme performed more effectively than existing schemes in terms of the embedded capacity, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).
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Tsai, Yuan-Yu, Yao-Hsien Huang, Ruo-Jhu Lin, and Chi-Shiang Chan. "An Adjustable Interpolation-based Data Hiding Algorithm Based on LSB Substitution and Histogram Shifting." International Journal of Digital Crime and Forensics 8, no. 2 (April 2016): 48–61. http://dx.doi.org/10.4018/ijdcf.2016040105.
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Data hiding can be regarded as a type of image processing techniques. Other image processing operations are usually integrated to increase the embedding capacity or decrease the visual distortion. Interpolation is an example of this type of operation. However, previous interpolation-based data hiding algorithms suffered from low and fixed embedding capacity and high visual distortion. This study proposes a more effective two-stage data hiding algorithm based on interpolation, LSB substitution, and histogram shifting. First, the authors modify the formula for embedding capacity calculation and make some adjustments on the sample pixels determination. A threshold is used to obtain the block complexity and each embeddable pixel has a different amount of message embedded. Second, an LSB substitution method and an optimal pixel adjustment process are adopted to raise the image quality. Finally, the authors' proposed algorithm can support adjustable embedding capacity. Compared to the previous algorithm, the experimental results demonstrate the feasibility of the proposed method.
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Wu, Nan-I., Kuo-Chen Wu, and Chung-Ming Wang. "Exploring pixel-value differencing and base decomposition for low distortion data embedding." Applied Soft Computing 12, no. 2 (February 2012): 942–60. http://dx.doi.org/10.1016/j.asoc.2011.09.002.
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Chari, Tara, and Lior Pachter. "The specious art of single-cell genomics." PLOS Computational Biology 19, no. 8 (August 17, 2023): e1011288. http://dx.doi.org/10.1371/journal.pcbi.1011288.
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Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce “all-in-one” visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.
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Yin, Te Lung, and Xu Ren Luo. "Reversible Data Hiding Exploiting Coefficients Histogram in Wavelet Transform Domain." Applied Mechanics and Materials 284-287 (January 2013): 3164–67. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3164.
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Reversible data hiding has drawn lots of interest in the last a few years. With reversibility, original media can be recovered without any distortion from the marked media after the embedded data has been extracted. In this paper, we present a new scheme which utilizes the wavelet transform and better exploited large variance of wavelet coefficient differences to achieve high capacity and imperceptibility. With the particularity of minor changes in the wavelet coefficients after embedding data, low visual distortion can therefore be obtained in the marked image. Furthermore, an extraordinary attribute of our scheme is that the use of embedding level differs greatly from previous schemes. Experimental results showed that the performance our scheme outperforms the state-of-the-art reversible data hiding schemes.
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Pan, Yi-Lun, and Ja-Ling Wu. "Rate-Distortion-Based Stego: A Large-Capacity Secure Steganography Scheme for Hiding Digital Images." Entropy 24, no. 7 (July 15, 2022): 982. http://dx.doi.org/10.3390/e24070982.
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Steganography is one of the most crucial methods for information hiding, which embeds secret data on an ordinary file or a cover message for avoiding detection. We designed a novel rate-distortion-based large-capacity secure steganographic system, called rate-distortion-based Stego (RD-Stego), to effectively solve the above requirement. The considered effectiveness of our system design includes embedding capacity, adaptability to chosen cover attacks, and the stability of the trained model. The proposed stego scheme can hide multiple three-channel color images and QR codes within another three-channel color image with low visual distortion. Empirically, with a certain degree of robustness against the chosen cover attack, we state that the system offers up to 192+ bits-per-pixel (bpp) embedding of a payload and leaks no secret-related information. Moreover, to provide theoretical foundations for our cost function design, a mutual information-based explanation of the choices of regulation processes is herein included. Finally, we justify our system’s claimed advantages through a series of experiments with publicly available benchmark datasets.
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Tsai, Yuan-Yu, Yu-Shiou Tsai, and Chia-Chun Chang. "An Effective Authentication Algorithm for Polygonal Models with High Embedding Capacity." Symmetry 10, no. 7 (July 18, 2018): 290. http://dx.doi.org/10.3390/sym10070290.
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Fragile watermarking algorithms for 3D models has attracted extensive research attention in recent years. Although many literatures have proposed lots of solutions on this issue, low embedding capacity and inaccurate located tampered region are still presented. Higher embedding capacity can reduce the probability of false alarms for authentication, while accurate tamper localization can detect all the modified vertices with fewer unaltered ones. This study proposes three strategies to enhance the embedding capacity and the detection accuracy of our previous algorithm. First, the modified message-digit substitution table can increase the embedding capacity by 11.5%. Second, the modified embedding ratio generation method can be integrated to raise the embedding capacity by about 47.74%. Finally, the strategy adopting a reduced number of reference vertices for authentication code generation accompanying the above two ones improves the embedding capacity up to 123.74%. Extensive experiments show that the proposed algorithm can achieve superior performance in terms of embedding capacity and tamper localization accuracy. Further, the model distortion between the original and the marked ones is small.
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Ma, Bin, Xiao-Yu Wang, and Bing Li. "A New Kind of High Capacity and Security Reversible Data Hiding Scheme." International Journal of Digital Crime and Forensics 11, no. 4 (October 2019): 118–29. http://dx.doi.org/10.4018/ijdcf.2019100108.
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A novel high capacity and security reversible data hiding scheme is proposed in this article, in which the secret data is represented by different orthogonal spreading sequences and repeatedly embedded into the cover image without disturbing each other in the light of Code Division Multiple Access (CDMA) technique, and thus the embedding capacity is enlarged. As most elements of orthogonal spreading sequences are mutually canceled in the process of repeated embedding, it keeps the distortion of the embedded image at a low level even with high embedding capacity. Moreover, only the receiver who has the spreading sequence and the embedding gain factor the same as the sender can extract the secret data and achieve the original image exactly, thus the proposed scheme achieves high embedding security than other schemes. The results of the experiment demonstrates that the CDMA based reversible data hiding scheme could achieve higher image quality at moderate-to-high embedding capacity compared with other state-of-the-art schemes.
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Chen, HaiShan, Jinye Wang, Yiqing Zhou, TingCheng Chang, KunQuan Shi, and JunYing Yuan. "Improving the Security of Reversible Data Hiding Using Multiple Histogram Modification." Mathematical Problems in Engineering 2022 (January 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/7192697.
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Reversible data hiding (RDH) allows carrying secret information in cover media without introducing permanent distortion. For a RDH method, the important performance measurements are embedding capacity and image quality. Since embedding capacity is an important requirement in the field of data hiding, it is necessary to consider the security of data embedding in RDH applications. In general, RDH algorithms usually prefer data embedding in simple image regions with low local complexity. As a result, image degradation is alleviated at the cost of poor embedding security. In this study, a novel RDH method is proposed to embed data into complex image regions, wherein the data hiding becomes more secure in defending against modern steganalysis. To measure regional local complexity, the harmonic mean of directional local variances is employed to combine directional pixel differences. To embed data into complex regions instead of smooth regions, multiple histogram modification is adopted and updated for optimized data embedding with higher complexity. Experiment results show that embedding security is significantly improved with a considerable amount of payload and well-preserved image quality.
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Juarez-Sandoval, Oswaldo Ulises, Laura Josefina Reyes-Ruiz, Francisco Garcia-Ugalde, Manuel Cedillo-Hernandez, Jazmin Ramirez-Hernandez, and Robert Morelos-Zaragoza. "Additional Information Delivery to Image Content via Improved Unseen–Visible Watermarking." Electronics 10, no. 18 (September 7, 2021): 2186. http://dx.doi.org/10.3390/electronics10182186.
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In a practical watermark scenario, watermarks are used to provide auxiliary information; in this way, an analogous digital approach called unseen–visible watermark has been introduced to deliver auxiliary information. In this algorithm, the embedding stage takes advantage of the visible and invisible watermarking to embed an owner logotype or barcodes as watermarks; in the exhibition stage, the equipped functions of the display devices are used to reveal the watermark to the naked eyes, eliminating any watermark exhibition algorithm. In this paper, a watermark complement strategy for unseen–visible watermarking is proposed to improve the embedding stage, reducing the histogram distortion and the visual degradation of the watermarked image. The presented algorithm exhibits the following contributions: first, the algorithm can be applied to any class of images with large smooth regions of low or high intensity; second, a watermark complement strategy is introduced to reduce the visual degradation and histogram distortion of the watermarked image; and third, an embedding error measurement is proposed. Evaluation results show that the proposed strategy has high performance in comparison with other algorithms, providing a high visual quality of the exhibited watermark and preserving its robustness in terms of readability and imperceptibility against geometric and processing attacks.
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Jin, Liu, and Yiwen Zhang. "An Improved NID Steganography Method for Low Bitrate Speech on VoIP." Modern Electronic Technology 5, no. 1 (May 6, 2021): 1. http://dx.doi.org/10.26549/met.v5i1.6167.
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In the procedure of encoding process on low bitrate speech, fixed codebook division is an efficient and promising embedding method for steganography. An improved neighbor index division (NID) steganography method based on the high bitrate frame of G.723.1 codec (6.3kbit/s) is proposed, which employs the parity and low distortion of neighbor indices for G.723.1 fixed codebooks. Differing from previously NID method which performs quantized index modulation (QIM) beforehand, the proposed method divides codeword indices into separate sub-codebooks according to the secret message bits dynamically in the original G.723.1 codec quantization period. Compared with existing NID method, our proposed method doesn’t need to divide the codebook before the encoding starts. The embedding and codebook dividing happen simultaneously, which utilizes the characteristics of specific secret message bits. The experiment results show that the proposed method has a much lower quality degradation for the decoding speech and still fulfills the low latency requirement for communication.
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Tsai, Yuan-Yu, Tsung-Chieh Cheng, and Yao-Hsien Huang. "A Low-Complexity Region-Based Authentication Algorithm for 3D Polygonal Models." Security and Communication Networks 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1096463.
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This study proposes a low-complexity region-based authentication algorithm for three-dimensional (3D) polygonal models, based on local geometrical property evaluation. A vertex traversal scheme with a secret key is adopted to classify each vertex into one of two categories: embeddable vertices and reference vertices. An embeddable vertex is one with an authentication code embedded. The algorithm then uses reference vertices to calculate local geometrical properties for the corresponding embeddable vertices. For each embeddable vertex, we feed the number of reference vertices and local properties into a hash function to generate the authentication code. The embeddable vertex is then embedded with the authentication code, which is based on a simple message-digit substitution scheme. The proposed algorithm is of low complexity and distortion-controllable and possesses a higher and more adaptive embedding capacity and a higher embedding rate than most existing region-based authentication algorithms for 3D polygonal models. The experimental results demonstrate the feasibility of the proposed algorithm.
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Kim, Cheonshik, Dong-Kyoo Shin, Ching-Nung Yang, and Lu Leng. "Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code." Applied Sciences 10, no. 15 (August 2, 2020): 5336. http://dx.doi.org/10.3390/app10155336.
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The image-based data hiding method is a technology used to transmit confidential information secretly. Since images (e.g., grayscale images) usually have sufficient redundancy information, they are a very suitable medium for hiding data. Absolute Moment Block Truncation Coding (AMBTC) is one of several compression methods and is appropriate for embedding data due to its very low complexity and acceptable distortion. However, since there is not enough redundant data compared to grayscale images, the research to embed data in the compressed image is a very challenging topic. That is the motivation and challenge of this research. Meanwhile, the Hamming codes are used to embed secret bits, as well as a block code that can detect up to two simultaneous bit errors and correct single bit errors. In this paper, we propose an effective data hiding method for two quantization levels of each block of AMBTC using Hamming codes. Bai and Chang introduced a method of applying Hamming (7,4) to two quantization levels; however, the scheme is ineffective, and the image distortion error is relatively large. To solve the problem with the image distortion errors, this paper introduces a way of optimizing codewords and reducing pixel distortion by utilizing Hamming (7,4) and lookup tables. In the experiments, when concealing 150,000 bits in the Lena image, the averages of the Normalized Cross-Correlation (NCC) and Mean-Squared Error (MSE) of our proposed method were 0.9952 and 37.9460, respectively, which were the highest. The sufficient experiments confirmed that the performance of the proposed method is satisfactory in terms of image embedding capacity and quality.
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Li, Jian Yong, Yan Qiu Yang, Qi Liang Tang, and Tong Wang. "A Watermarking of Two Dimensions Based on DCT." Applied Mechanics and Materials 263-266 (December 2012): 2901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2901.
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A watermark algorithm based on HVS in DCT domain is proposed. The original host image is scrambled with magic transform and divided into blocks and manipulates by DCT. The original image’s DCT coefficients can equably distribute in the coefficients matrix. The binary image is divided into 2×2 blocks before embedding the watermarking. Those blocks are random sequenced by the pseudorandom sequence, and then the watermarking image is scrambled by Arnold. The theory of HVS is used to decide the embedding strength under low distortion, which efficiently enhances the robustness of watermarking. The two basic factors of watermarking are robustness and invisibility. The key techniques which affect them are the means we choose to carry out digital watermarking and the watermarking structure we use.
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Wang, Jen-Tse, Yi-Ching Chang, Chun-Yuan Yu, and Shyr-Shen Yu. "Hamming Code Based Watermarking Scheme for 3D Model Verification." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/241093.
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Due to the explosive growth of the Internet and maturing of 3D hardware techniques, protecting 3D objects becomes a more and more important issue. In this paper, a public hamming code based fragile watermarking technique is proposed for 3D objects verification. An adaptive watermark is generated from each cover model by using the hamming code technique. A simple least significant bit (LSB) substitution technique is employed for watermark embedding. In the extraction stage, the hamming code based watermark can be verified by using the hamming code checking without embedding any verification information. Experimental results shows that 100% vertices of the cover model can be watermarked, extracted, and verified. It also shows that the proposed method can improve security and achieve low distortion of stego object.
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Leng, Hui-Shih. "Generalized Scheme Based on Octagon-Shaped Shell for Data Hiding in Steganographic Applications." Symmetry 11, no. 6 (June 4, 2019): 760. http://dx.doi.org/10.3390/sym11060760.
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The exploiting modification direction scheme is a well-known data hiding method because of its high payload and low distortion. Its most criticized drawback is that the secret data must be converted to a non-binary numeral system before the embedding procedure. To overcome this drawback, one study proposed a turtle shell-based scheme for data hiding, in which a reference matrix was constructed based on a hexagon-shaped shell to embed three secret bits into each pixel group of a pair of pixels of the cover image. In a subsequent work, the embedding capacity was increased by using an octagon-shaped shell instead of a hexagon-shaped shell in the reference matrix. The above scheme was extended by utilizing a regular octagon-shaped shell in the reference matrix to reduce the distortion of the worst case. In another approach, the payload of the octagon-shaped-shell-based data hiding scheme was maximized by searching for the appropriate width and height of the octagon. In this study, we generalize the data hiding scheme based on an octagon-shaped shell so that the above-mentioned four schemes can be regarded as specific cases of the proposed method.
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Pang, LieLin, KokSheik Wong, Yiqi Tew, and Susanto Rahardja. "Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting." Applied Sciences 11, no. 11 (May 25, 2021): 4850. http://dx.doi.org/10.3390/app11114850.
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With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.
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Zhao, Shuai, Jifeng Sun, Hongshi Ou, and Yibin Lin. "A Novel Multi-Task Face Super-Resolution Framework Embedding Degraded Augmented GAN Networks." Journal of Physics: Conference Series 2303, no. 1 (July 1, 2022): 012061. http://dx.doi.org/10.1088/1742-6596/2303/1/012061.
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Abstract The training method that uses predefined degradation methods to provide training data for deep convolutional neural networks suffers from overfitting and image distortion when performing face super-resolution reconstruction. In this paper, we propose a multi-task facial super-resolution reconstruction framework embedded in a degraded augmented GAN network. Compared with the commonly used method of generating low-quality face images through pixel interpolation, this paper inversely embeds a generative adversarial network composed of residual coding blocks in the network. Through the feature learning ability of the network, the feature fitting of the face images collected in the natural scene is performed to generate training data that can more fully represent the real noise in the natural scene, thereby improving the noise reduction and image reconstruction capabilities of the network. In order to take full advantage of the task-specific features, the semantic features of the dataset are extracted through a pre-trained facial organ semantic detection framework and the data is used to add face semantic information in the super-resolution reconstruction network. The experimental results show that the model proposed in this study can achieve super-resolution reconstruction of ultra-low-resolution face images simply and efficiently, and achieve super-resolution face images with lower visual quality than other advanced methods with lower model complexity. It solves the problem of single style and distortion of face reconstruction images caused by the training method of the data set obtained by the usual predefined degradation.
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Chen, Weitong, Na Ren, Changqing Zhu, Qifei Zhou, Tapio Seppänen, and Anja Keskinarkaus. "Screen-Cam Robust Image Watermarking with Feature-Based Synchronization." Applied Sciences 10, no. 21 (October 25, 2020): 7494. http://dx.doi.org/10.3390/app10217494.
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The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.
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Yongjun, Kong, Zhang Minqing, Wang Zexi, Ke Yan, and Huang Siyuan. "Reversible Data Hiding in Encrypted Domain Based on the Error-Correction Redundancy of Encryption Process." Security and Communication Networks 2022 (May 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/6299469.
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The existing reversible data hiding methods in encrypted domain separate image encryption from information embedding and do not make full use of the redundant space in the process of image encryption. In order to improve the performance of reversible data hiding by using the technical characteristics of image encryption, a reversible data hiding method based on McEliece error correction is proposed. Firstly, the segmentation position of bit plane is determined according to the embedding requirement and texture characteristic, and the image is divided into high and low significant bits. Secondly, because of the error-correcting characteristic of McEliece encryption, reversible data embedding can be realized while encrypting low significant bits. Then, the high significant bits are compressed to reserve space for the ciphertext extension of the low significant bits. Finally, the whole high significant bits information is stream-encrypted. As long as the image receiver has the decryption key, the image can be restored without distortion. By concealing the relationship between error correction and secret information mapping, the concealment of secret information transmission can be realized. In addition, due to different processing for different pixels, it can be efficiently transmitted with low computational complexity for applications that only need general images. The simulation results show that this scheme can not only realize the separable operation of information extraction and image recovery but also resist the noise attack to a certain extent. The maximum embedding rates of 10 standard images from USC-SIPI and 50 standard images from BOSS-BASE are 2.228 and 2.323 bpp, respectively.
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Maity, Hirak Kumar, and Santi P. Maity. "FPGA Implementation for Modified RCM-RW on Digital Images." Journal of Circuits, Systems and Computers 26, no. 03 (November 21, 2016): 1750044. http://dx.doi.org/10.1142/s021812661750044x.
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Reversible contrast mapping (RCM) and its various modified forms are used extensively in reversible watermarking (RW) on digital images to embed secret information. Sometimes RW demands real-time implementation that can be made by hardware realization and RCM-based RW becomes appealing due to its integer transform nature followed by least significant bit (LSB) substitution for embedding. This motivates us to propose RCM-RW on digital images with two objectives: modification on the existing RCM algorithm to increase embedding rate with low visual distortion. Later on, prototype design through field-programmable gate array (FPGA) is developed for a (256 × 256), 8-bit gray scale image using (32 × 32) image block and pipeline architecture. For a cover image of size (256 × 256), the proposed architecture requires 8,971 slices, 8,246 slice flip-flops, 9,706 numbers of four-input LUTs and 2 FIFO 16/RAMB for watermark embedding purpose. Proposed architecture consumes a power of 636[Formula: see text]mW and offers a data rate of 1.0493[Formula: see text]Mbps at an operating frequency as high as 95.3[Formula: see text]MHz.
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Chen, Tzung-Her, and Jing-Ya Yan. "Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity." Applied Sciences 13, no. 15 (August 1, 2023): 8865. http://dx.doi.org/10.3390/app13158865.
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High-dynamic-range (HDR) images are widely regarded as the ideal format for digital images due to their ability to accurately render a wider range of luminance values. Recently, research has focused on introducing data-hiding techniques to HDR images, but these studies often suffer from a low hiding capacity. In 2011, a steganography scheme was proposed, which utilizes homogeneity in RGBE (red, green, blue, and exponent) format, a popular HDR format, and results in cover images with only slight and ignorable distortions after embedding. However, the capacity of the scheme is limited, and their steganography process may raise suspicions due to the abnormal distribution of pixel values caused by the multiplication and division in the embedding process. There is no denying that security is always the main concern for steganography. A major potential problem became clear after a careful revisiting of the scheme. This paper presents an enhanced steganography scheme that improves embedding capacity by modifying non-embeddable pixels to become embeddable in cover images and avoids potential security weaknesses by using additional random numbers to alter pixel values. The proposed scheme improves the embedding capacity of HDR images while maintaining their visual quality and security against statistical analysis attacks. The experimental result shows that the capacity increases 10 times without visual distortion.
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Tan, Xiu Hu. "A Blind Watermarking Method Based on Space Reconstruction." Applied Mechanics and Materials 444-445 (October 2013): 712–16. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.712.
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Watermarking embeds information into a digital signal like audio, image, or video. Reversible image watermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, we propose a blind watermarking method based on space reconstruction. Through calculating the statistical property of the digital image, we are able to separately obtain the feature spaces. Passing spaces decomposing and reconstructing of the feature spaces, constructing the embedding matrix, we obtain that the robustness of the approach lies in hiding a watermark in the subspace that is the least susceptible to potential modification; and realize the optimization statistics of the embedding watermark. Through analysis and constraint the conditions of subspace, the algorithm we proposed can obtain a high detection probability and security, a low false alarm probability. The robustness of the watermarking method is demonstrated by a kind of attacks through computer simulation.
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Huang, Dan, and Fangjun Huang. "Reversible Data Hiding Based on Adaptive Block Selection Strategy." International Journal of Digital Crime and Forensics 12, no. 1 (January 2020): 157–68. http://dx.doi.org/10.4018/ijdcf.2020010108.
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Recently, a reversible data hiding (RDH) method was proposed based on local histogram shifting. This method selects the peak bin of the local histogram as a reference and expands the two neighboring bins of the peak bin to carry the message bits. Since the peak bin keeps unchanged during the embedding process, the neighboring bins can be easily identified at the receiver end, and the original image can be restored completely while extracting the embedded data. In this article, as an extension of the algorithm, the authors propose an RDH scheme based on adaptive block selection strategy. Via a new block selection strategy, those blocks of the carrier image may carry more message bits whereas introducing less distortion will take precedence over data hiding. Experimental results demonstrate that higher visual quality can be obtained compared with the original method, especially when the embedding rate is low.
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Zhang, Shanqing, Hui Li, Li Li, Jianfeng Lu, and Ziqian Zuo. "A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks." Sensors 22, no. 20 (October 15, 2022): 7844. http://dx.doi.org/10.3390/s22207844.
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Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.
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Chi, Heng-Xiao, Ji-Hwei Horng, Chin-Chen Chang, and Yung-Hui Li. "Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy." Sensors 22, no. 20 (October 18, 2022): 7942. http://dx.doi.org/10.3390/s22207942.
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How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block’s dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block’s interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.
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M, Abhinaya, and A. Umamakeswari. "A Robust Image Watermarking Method based on Contourlet Transform for Correcting Geometric Attacks." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 24. http://dx.doi.org/10.14419/ijet.v7i2.24.11992.
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A watermarking technique is proposed using Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) techniques to implement information hiding. Embedding can be done by dividing the host image into four rectangular segments which are non-overlapping called sub-images using hybrid scheme. Redundancy reduces cropping attack. The main aim is to reduce the effect of geometric bouts, such as rotation, translation, and affine translation using synchronization technique. Watermarks can be a binary random sequence of different lengths. Data replication and hamming code are two error modification methods utilized in the proposed scheme. Achieved results show comparable robustness against geometric attacks and signal processing. Contourlet transform can be used to improve the visual feature of the image. After embedding the image or data, contourlet transform is applied in the frequency domain to improve robustness. The optimal mapping function is obtained using contourlet transform which increases Peak Signal to Noise Ratio (PSNR) and hiding capacity with low distortion.
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Bogacki, Piotr, and Andrzej Dziech. "Analysis of New Orthogonal Transforms for Digital Watermarking." Sensors 22, no. 7 (March 29, 2022): 2628. http://dx.doi.org/10.3390/s22072628.
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The paper focuses on the application of new orthogonal transforms in digital watermarking. Novel types of transforms and their characteristics are presented. Potential methods for watermark embedding and recovery are also proposed. They assume embedding hidden information in the transform domains using the luminance channel of the original image. Image spectra are obtained by dividing the original image into smaller blocks that then are further processed by performing the forward transform operation. A watermark is embedded by modifying the spectral coefficients with relatively low values. Since there are various types of transforms, the latter process is realized in an adaptive manner. The proposed solutions were evaluated by measuring the level of visual distortion with respect to the total size of the inserted data. Additionally, the bit error rate (BER) in the recovery phase is also analyzed. The elaborated methods seem to be useful for applications in digital signal and image processing where high imperceptibility and low BER are of great importance. New orthogonal transforms were proved to be useful in watermarking tasks, and in some cases, they can even outperform the classic DCT approach.
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Lee, Yeuan-Kuen, and Ling-Hwei Chen. "Secure Error-Free Steganography for JPEG Images." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 06 (September 2003): 967–81. http://dx.doi.org/10.1142/s021800140300268x.
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The typical model of steganography has led the prisoners' problem, in which two persons attempt to communicate covertly without alerting the warden, that is, only the receiver knows the existence of the message sent by the sender. One available way to achieve this task is to embed the message in an innocuous-looking medium. In this paper, we propose a variation of the Quantization Index Modulation (QIM) for solving the prisoners' problem. We also propose a theorem to show that the error of mean intensity value of an image block caused by JPEG compression is bounded. The proposed method embeds the messages to be conveyed by modifying the mean intensity value, and the resulting stego-image can be stored in the JPEG format with a low quality setting. Besides, a specific pattern caused by using the QIM embedding method is also identified, and this pattern will be removed using the proposed embedding method. Experimental results and the proposed theorem show that the hidden message is error-free against the JPEG distortion under the quality setting as low as 25. Furthermore, the existence of the hidden message is not only visually imperceptible but also statistically undetectable.
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Chang, Ching-Chun. "Neural Reversible Steganography with Long Short-Term Memory." Security and Communication Networks 2021 (April 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/5580272.
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Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.
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Progonov, Dmytro. "Multi-Datasets Evaluation of GB-RAS Network Based Stegdetectors Robustness to Domain Adaptation Problem." Information Theories and Applications 28, no. 4 (2021): 372–96. http://dx.doi.org/10.54521/ijita28-04-p04.
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Protection of state and commercial critical infrastructures is topical task today. Among known threats, special interest is taken to early detection of hidden (steganographic) channels usage by attackers for unauthorized transmission of sensitive data. These channels are created by modification of the innocuous files, such as digital images, for message hiding and further transmission of modified files. Detection of formed stego files is non-trivial task due to the wide usage by attackers of adaptive embedding methods that preserve low level of cover image distortion during embedding. Modern solutions for stego images detection are based on utilization the novel convolution neural networks for revealing weak alterations of cover’s features during message hiding. Training of such networks is performed on fixed set of standard image databases, such as BOSS, ALASKA etc. Despite high detection accuracy of pre-trained stegdetectors, theirs performance “in the wild” on natural images remains an open question. The work is devoted to performance analysis of advanced GB-Ras convolutional network based stegdetector for adaptive embedding methods detection on natural images presented in various datasets. Obtained results proved the effectiveness of neural network applying for mitigation with domain adaptation problem for modern stegdetectors. The GB-Ras model allows improving detection accuracy of stego images for standard ALASKA (up to 6% for for S-UNIWARD embedding methods) and VISION (up to 13% for S-UNIWARD and to 9% for MG embedding methods) datasets in comparison with cover rich models based stegdetectors. Nevertheless, the performance of considered GB-Ras based stegdetector for high quality images from MIRFlickr dataset is conceded to maxSRMd2 rich model based stegdetector (up to 11% for S-UNIWARD and 12% for MG embedding methods). Therefore, considered GB-Ras network may be used as a promising candidate for further design of novel stegdetectors that are robust to domain adaptation problem.
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WANG, Junxiang, Jiangqun NI, Dong ZHANG, and Hao LUO. "Improved Histogram Shifting Technique for Low Payload Embedding by Using a Rate-Distortion Model and Optimal Side Information Selection." IEICE Transactions on Information and Systems E95.D, no. 10 (2012): 2552–55. http://dx.doi.org/10.1587/transinf.e95.d.2552.
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Ma, Bin, Bing Li, Xiao-Yu Wang, Chun-Peng Wang, Jian Li, and Yun-Qing Shi. "Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image." Security and Communication Networks 2019 (January 17, 2019): 1–9. http://dx.doi.org/10.1155/2019/4732632.
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In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.
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Lee, Jenny Hyunjung, Darius Coelho, and Klaus Mueller. "Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts." Information 13, no. 1 (December 23, 2021): 3. http://dx.doi.org/10.3390/info13010003.
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Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions occur both at the global inter-cluster and the local intra-cluster levels. The former leads to misinterpretation of the distances between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance. The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance. In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design. Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots. We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors. Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D statistics to yield what we call Cluster Appearance Glyphs. We demonstrate our framework with a dataset acquired to analyze program execution times in file systems.
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Lian, Lu, and Tong Lei. "Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology." Journal of Mathematics 2022 (January 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/6331233.
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In order to study the application of computer digital image processing technology in film and television (FAT) animation visual sensing expression, by studying the principle of digital image processing technology and visual sensing technology, a spatial image adaptive steganography image enhancement algorithm by multiscale filters is proposed to carry out enhancement processing of the original image in FAT production. This algorithm can provide more high-quality and refined original materials for FAT animation production, which is convenient for FAT animation postproduction to produce higher-resolution and clear FAT works. Finally, the algorithm is verified. The results show that the spatial image adaptive steganography image enhancement algorithm has high security, and the highest average detection error rate is 25.06%. When α = 0.4 , the security of the spatial image adaptive steganography image enhancement algorithm is up to 34.62% and the image distortion rate is low. The established image enhancement algorithm can significantly improve the security of the existing spatial image steganography algorithm under different embedding rates, especially at a high embedding rate; the improvement of the spatial domain steganography algorithm is greater. The proposed steganographic image enhancement algorithm by image preprocessing has higher security and better image enhancement effect.
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Hemis, Mustapha, Bachir Boudraa, and Thouraya Merazi-Meksen. "New secure and robust audio watermarking algorithm based on QR factorization in wavelet domain." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 03 (May 2015): 1550020. http://dx.doi.org/10.1142/s0219691315500204.
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Digital watermarking consists in embedding imperceptible information into a host signal. It has been proposed to solve problems as varied as the protection of the copyright, content authentication, fingerprinting and broadcast monitoring. This paper presents a new approach for audio watermarking using the QR factorization in wavelet domain. This approach is based on embedding a watermark binary image in the R matrices of low frequency blocks DWT coefficients of audio signal. In this algorithm, the watermark is embedded by applying a Quantization Index Modulation (QIM) process on the determined optimal sample for each matrix R. The watermark can be blindly extracted without the knowledge of the original audio signal. Experimental results show that the proposed audio watermarking scheme maintains high quality of the audio signal. Signal to Noise Ratio (SNR), Log Spectral Distortion (LSD) and Mean Opinion Score (MOS) are about 40 dB, 0.37 dB and 4.84, respectively. Moreover, the scheme is quite robust against common signal processing attacks such as noise addition, filtering and MP3 compression. In addition, this method ensures a secure extraction process by using a private key, making it suitable for secure applications such as copyright protection.
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Fang Ren, Fang Ren, Yi-Ping Yang Fang Ren, and Zhe-Lin Zhang Yi-Ping Yang. "Dynamic Hybrid Reversible Data Hiding Based on Pixel-value-ordering." 電腦學刊 34, no. 6 (December 2023): 015–29. http://dx.doi.org/10.53106/199115992023123406002.
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<p>Reversible data hiding (RDH) using pixel-value-ordering (PVO) is a well-established technique for embedding data in a cover image by modifying the maximum and minimum in each block. This paper proposes a dynamic hybrid RDH method based on PVO. Specifically, a 3×3 block according to its complexity and two thresholds T1 and T2 is classified as three levels: extremely smooth, smooth, and rough. Different processing algorithms are used for different levels. For rough blocks, they are ignored to avoid reducing the peak signal-to-noise ratio. For smooth blocks, the proposed method employs a block subdivision algorithm that can embed up to 6 bits of data. For extremely smooth blocks, no subdivision is done and a median pixel prediction algorithm is used to predict the remaining eight pixels, which can embed up to 8 bits of data. Moreover, this paper presents a new method that computes complexity by dynamically selecting relevant pixels to enhance block classification accuracy. Extensive experiments demonstrate that the proposed method outperforms existing PVO-based methods, offering larger embedding capacity while maintaining low distortion.</p> <p> </p>
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Altimime, Sajad, and Zainab Hussain. "Watermarking Using Energy-LSB Embedded Method." Wasit Journal of Computer and Mathematics Science 1, no. 3 (October 1, 2022): 140–48. http://dx.doi.org/10.31185/wjcm.53.
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Digital watermarking technology is increasingly used to protect copyright and demonstrate ownership of digital multimedia (such as text, music, photos, and videos). In order to safeguard intellectual property rights and rules of ownership for multimedia, this project suggests a text watermark algorithm. The process of hiding little text or grayscale images is the main focus, though. The masking of a watermark text in a high-color or high-density area of the block picture has been proposed using an embedding technique based on an energy function and the Least Significant Bits (LSB) method. Even with various format types and picture sizes chosen to conceal and cover a changing message size, the quality results demonstrate that the watermark image suffers from less distortion than the cover image, and the suggested algorithm is powerful to conceal a random watermark text even with smaller block sizes. An additional optional choice to encrypt the text watermark before embedding is also recommended because doing so would make it harder for hackers to read the text. This text can be encrypted using the Caesar cipher method before embedding is implemented in images.The experimental results of using the suggested algorithm for embedding and extracting watermark text for various sizes in a large number of images were satisfactory, giving a level of peak signal-to-noise ratio (PSNR) and Signal to Noise Ratio (SNR) with low mean square error (MSE) values. However, PSNR degrades more quickly than LBS as the watermark text size increases, so it was determined that it is more suitable for applying a watermark rather than a stego It is employed in order to share information securely
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Denisiuk, Aleksander, and Michał Grabowski. "Low Distortion Embedding of the Hamming Space into a Sphere with Quadrance Metric and k-means Clustering of Nominal-continuous Data." Fundamenta Informaticae 153, no. 3 (June 16, 2017): 221–33. http://dx.doi.org/10.3233/fi-2017-1538.
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Alex Rajju Balan, J. A., and S. Edward Rajan. "A novel embedding technique for lossless data hiding in medical images employing histogram shifting method." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (May 2014): 1450026. http://dx.doi.org/10.1142/s021969131450026x.
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In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.
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Ma, Rongkai, Pengfei Fang, Tom Drummond, and Mehrtash Harandi. "Adaptive Poincaré Point to Set Distance for Few-Shot Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1926–34. http://dx.doi.org/10.1609/aaai.v36i2.20087.
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Learning and generalizing from limited examples, i.e., few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learning is to realize an embedding where samples from different classes are distinctive. Recent studies suggest that embedding via hyperbolic geometry enjoys low distortion for hierarchical and structured data, making it suitable for few-shot learning. In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively, based on the constellation of the points. This not only makes the metric local but also dependent on the task in hand, meaning that the metric will adapt depending on the samples that it compares. We empirically show that such metric yields robustness in the presence of outliers and achieves a tangible improvement over baseline models. This includes the state-of-the-art results on five popular few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds-200-2011(CUB), CIFAR-FS, and FC100.
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Kim, Cheonshik, Nhu-Ngoc Dao, Ki-Hyun Jung, and Lu Leng. "Dual Reversible Data Hiding in Encrypted Halftone Images Using Matrix Encoding." Electronics 12, no. 14 (July 19, 2023): 3134. http://dx.doi.org/10.3390/electronics12143134.
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Data hiding and reversible data hiding research has primarily focused on grayscale and color images, because binary and halftone images are prone to visual distortion caused by a small number of errors in pixel representation. As a result, reversible data hiding is more useful than halftone-based data hiding. This study proposes an investigation of encrypted halftone images based on dual reversible data hiding, which improves the reversibility and security of the image by utilizing a dual cover image. Since halftone images are adequately compressed, they are beneficial in low-channel-bandwidth environments. Hamming code (HC) (7,4) is applied to each block of the halftone image to hide the secret data, and two halftone images are recorded and sent to different receivers at the end of the embedding process. Recipients can use the proposed method and the two marked images to extract the message and recover the cover halftone image. The proposed data hiding method can enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing the block size. The experimental results provide evidence of the effectiveness of the proposed method in terms of both image quality and the embedding rate.
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Alhaddad, Mohammed J., Monagi H. Alkinani, Mohammed Salem Atoum, and Alaa Abdulsalm Alarood. "Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things." Symmetry 12, no. 12 (December 14, 2020): 2071. http://dx.doi.org/10.3390/sym12122071.
Full textAbstract:
With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.