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Journal articles on the topic 'Image tamperings'
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1
Gaffar, Achmad Fanany Onnilita, Supriadi Supriadi, Arief Bramanto Wicaksono Saputra, Rheo Malani, and Agusma Wajiansyah. "A Splicing Technique for Image Tampering using Morphological Operations." Signal and Image Processing Letters 1, no. 2 (July 19, 2019): 36–45. http://dx.doi.org/10.31763/simple.v1i2.4.
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Image tampering is one part of the field of image editing or manipulation that changes certain parts of the graphic content of a given image. There are several techniques commonly used for image tampering, such as splicing, copy-move, retouching, etc. Splicing is a type of image tampering technique that combines two different images, replacing particular objects, skewing, rotation, etc. This study applies the splicing technique to image tampering using morphological operations. Morphology is a collection of image processing operations that process images based on their shape. The aim of this study is to replace particular objects in an original image with other objects that are similar to another selected image. In this study, we try to replace the ball object in the original image with another ball object from another image
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Singhania, Shruti, Arju N.A, and Raina Singh. "Image Tampering Detection Using Convolutional Neural Network." International Journal of Synthetic Emotions 10, no. 1 (January 2019): 54–63. http://dx.doi.org/10.4018/ijse.2019010103.
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Pictures are considered the most reliable form of media in journalism, research work, investigations, and intelligence reporting. With the rapid growth of ever-advancing technology and free applications on smartphones, sharing and transferring images is widely spread, which requires authentication and reliability. Copy-move forgery is considered a common image tampering type, where a part of the image is superimposed with another image. Such a tampering process occurs without leaving any obvious visual traces. In this study, an image tampering detection method was proposed by exploiting a convolutional neural network (CNN) for extracting the discriminative features from images and detects whether an image has been forged or not. The results established that the optimal number of epochs is 50 epochs using AlexNet-based CNN for classification-based tampering detection, with a 91% accuracy.
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Agarwal, Ritu, and Mallika Pant. "Image tampering detection using genetic algorithm." MATEC Web of Conferences 277 (2019): 02026. http://dx.doi.org/10.1051/matecconf/201927702026.
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As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.
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Li, Su Ying. "Round Descriptor Based on SIFT Operator Copy and Paste Forensics." Advanced Materials Research 912-914 (April 2014): 1379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1379.
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Powerful digital cameras and image processing software, so that the image of tampering counterfeit technology is developing rapidly, the role of digital images to verify the authenticity and the beginning of forensics technology will become more important. For the most common digital image tampering copy and paste operation, this paper presents a round-based SIFT descriptor operator forensic image copy and paste algorithm that extracts the image to be detected circular feature vector descriptor, and using feature vectors copy and paste the image matching to detect and locate the area. Experiments show that the method of image rotation, zoom, blur, noise and other image processing operations are robust, able to quickly and effectively detect tampering in digital images traces copy and paste operations, and is able to copy and paste the region for accurate positioning.
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Zhan, Cai, Lu Leng, Chin-Chen Chang, and Ji-Hwei Horng. "Reversible Image Fragile Watermarking with Dual Tampering Detection." Electronics 13, no. 10 (May 11, 2024): 1884. http://dx.doi.org/10.3390/electronics13101884.
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The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved.
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Chang, Yu Hong. "Based on Digital Image Forensics Double JPEG Compression Algorithm." Advanced Materials Research 1044-1045 (October 2014): 1098–101. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1098.
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Means of digital image tampering diverse, new technical approach increasingly renovation, therefore, the detection of forged digital tampering issues are complex, and must not universal, once and for all solutions. In this paper, digital image tampering common practices through research, proposed a re-deposit operations for double JPEG image processing software compression testing technology method using image compression and decompression process lossy compression of images tamper with forensic, this method can play an effective role in the detection.
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Lee, Chin-Feng, Chin-Ting Yeh, Jau-Ji Shen, and Taeshik Shon. "Image Authentication and Restoration Using Block-Wise Variational Automatic Encoding and Generative Adversarial Networks." Electronics 12, no. 16 (August 10, 2023): 3402. http://dx.doi.org/10.3390/electronics12163402.
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The Internet is a conduit for vast quantities of digital data, with the transmission of images being especially prevalent due to the widespread use of social media. However, this popularity has led to an increase in security concerns such as image tampering and forgery. As a result, image authentication has become a critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing image authentication techniques using deep learning to combat various image tampering attacks. Nevertheless, image authentication techniques based on deep learning typically classify only specific types of tampering attacks and are unable to accurately detect tampered images or indicate the precise location of tampered areas. The paper introduces a novel image authentication framework that utilizes block-wise encoding through Variational Autoencoder and Generative Adversarial Network models. Additionally, the framework includes a classification mechanism to develop separate authentication models for different images. In the training phase, the image is first divided into blocks of the same size as training data. The goal is to enable the model to judge the authenticity of the image by blocks and to generate blocks similar to the original image blocks. In the verification phase, the input image can detect the authenticity of the image through the trained model, locate the exact position of the image tampering, and reconstruct the image to ensure the ownership.
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Zeng, Pingping, Lianhui Tong, Yaru Liang, Nanrun Zhou, and Jianhua Wu. "Multitask Image Splicing Tampering Detection Based on Attention Mechanism." Mathematics 10, no. 20 (October 17, 2022): 3852. http://dx.doi.org/10.3390/math10203852.
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In today’s modern communication society, the authenticity of digital media has never been of such importance as it is now. In this aspect, the reliability of digital images is of paramount importance because images can be easily manipulated by means of sophisticated software, such as Photoshop. Splicing tampering is a commonly used photographic manipulation for modifying images. Detecting splicing tampering remains a challenging task in the area of image forensics. A new multitask model based on attention mechanism, densely connected network, Atrous Spatial Pyramid Pooling (ASPP) and U-Net for locating splicing tampering in an image, AttDAU-Net, was proposed. The proposed AttDAU-Net is basically a U-Net that incorporates the spatial rich model filtering, an attention mechanism, an ASPP module and a multitask learning framework, in order to capture more multi-scale information while enlarging the receptive field and improving the detection precision of image splicing tampering. The experimental results on the datasets of CASIA1 and CASIA2 showed promising performance metrics for the proposed model (-scores of 0.7736 and 0.6937, respectively), which were better than other state-of-the-art methods for comparison, demonstrating the feasibility and effectiveness of the proposed AttDAU-Net in locating image splicing tampering.
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Mire, Archana V., Sanjay B. Dhok, Narendra J. Mistry, and Prakash D. Porey. "Localization of Tampering Created with Facebook Images by Analyzing Block Factor Histogram Voting." International Journal of Digital Crime and Forensics 7, no. 4 (October 2015): 33–54. http://dx.doi.org/10.4018/ijdcf.2015100103.
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Facebook images get distributed within a fraction of a second, which hackers may tamper and redistribute on cyberspace. JPEG fingerprint based tampering detection techniques have major scope in tampering localization within standard JPEG images. The majority of these algorithms fails to detect tampering created using Facebook images. Facebook utilizes down-sampling followed by compression, which makes difficult to locate tampering created with these images. In this paper, the authors have proposed the tampering localization algorithm, which locates tampering created with the images downloaded from Facebook. The algorithm uses Factor Histogram of DCT coefficients at first 15 modes to find primary quantization steps. The image is divided into BXB overlapping blocks and each block is processed individually. Votes cast by these modes for conceivable tampering are collected at every pixel position and the ones above threshold are used to form different regions. High density voted region is proclaimed as tampered region.
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Thabit, Rasha, Jaffer Ali, and Doaa Subhi. "Tampering Reveal Technique for Iris Images." International Journal of Computer Networks and Communications Security 8, no. 6 (June 30, 2020): 46–51. http://dx.doi.org/10.47277/ijcncs/8(6)1.
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Nowadays, many biometric based security systems depend on the iris images for authentication because of their features and ease of use. However, storing and sharing these sensitive images through open access networks will expose them to tampering. In order to ensure the safety of the iris images, this paper presents a new tampering reveal technique based on watermarking in the transform domain. In the proposed technique, the iris region is selected and isolated using interactive segmentation process (ISA). The authentication bits have been generated from the iris region (IR) and embedded in the Slantlet transform coefficients of the remaining part of the iris image which has been named as non-iris region (NIR). The use of ISA ensures the intactness of IR because it has been excluded from the embedding process. Several experiments have been conducted to test the visual quality, capacity, payload, and the tampering reveal performance. The experiments proved the ability of the proposed technique to reveal and localize any tampering in IR, in addition, the difference between the original iris image and the watermarked iris image is imperceptible
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Jena, Riyanka, Priyanka Singh, and Manoranjan Mohanty. "PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization." Journal of Imaging 9, no. 9 (August 27, 2023): 172. http://dx.doi.org/10.3390/jimaging9090172.
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The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10×10 in the encrypted domain. Furthermore, the method identifies the featured image’s JPEG quality using the first minima in the energy graph.
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Doegar, Amit, Srinidhi Hiriyannaiah, G. M. Siddesh, K. G. Srinivasa, and Maitreyee Dutta. "Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics." BioMed Research International 2021 (April 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/5546572.
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Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.
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Abbadi, Nidhal, and Alyaa Mohsin. "Blind Digital Images Tampering Detection Based on Singular Value Decomposition." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 338–48. http://dx.doi.org/10.22266/ijies2020.1231.30.
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The growing use of digital images in a wide range of applications, and growing the availability of many editing photo software, cause to emerge a challenge to discover the images tampering. In this paper, we proposed a method to detect the most important type of forgery image (copy and move). We suggested many steps to classify the image as forgery or non-forgery image, started with preprocessing (included, convert image to gray image, de-noising, and image resize). Then, the image will be divided into several overlapping blocks. For each block, feature extracted (used it as a matching feature) by using the singular value decomposition (SVD) transformation. According to these features, the pixels were collected in many main groups, and then these groups clustered to many subgroups. The weight for each main group can be determined by comparing the subgroups with each other according to suggested conditions. The number of subgroups and weights are used to classify images to forgery or non-forgery images. The accuracy of detection and classified the forgery images were up to 97%. The suggested method is robust for tampered object rotation, scaling, and change of illumination.
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Vijayaraghavan, P., M. Joe Nelson, R. Prasanna, and M. K. Raghavendran. "Surveillance Footage Video Tampering Detection." Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems 01, no. 01 (December 1, 2020): 10–16. http://dx.doi.org/10.47812/ijamecs2010102.
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The quality of the captured images was enhanced by some of the first digital video and image processing applications, but as the power of computers expanded, so did the number of applications where video and image processing could make a difference. Video and image processing are used in many different applications nowadays. To provide authenticity to a video several video processing software’s are used. Video tampering detection is the one of the important Security applications. Video processing software is also used to eliminate specific moving foreground from a video. But algorithms which were proposed earlier find it difficult when it comes to a video with complex background. So, in this paper an attempt is made to overcome those problem, the video foreground elimination is done by using a novel forgery detection algorithm. It is found that this method is effective when compared to the other algorithms.
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Hu, Yuan-Yuan, Hao Luo, and Xia-Mu Niu. "Image hashing framework for tampering localization in distorted images." IEICE Electronics Express 7, no. 22 (2010): 1679–85. http://dx.doi.org/10.1587/elex.7.1679.
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Sathish, A., R. Aasha, P. Abinayasri, and B. Kaviya. "Cyber vaccinator for image tamper resilient and recovery using invertible neural network." i-manager's Journal on Digital Forensics & Cyber Security 2, no. 1 (2024): 27. http://dx.doi.org/10.26634/jdf.2.1.21059.
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People frequently interact with their families, friends, and colleagues through Online Social Networks (OSNs). People post and share their photos in online communities and content-sharing sites. The problem addressed in this paper is the susceptibility of digital images to tampering, which compromises security and privacy. Traditional image forgery detection methods face challenges in reproducing original content after manipulation. This paper introduces an advanced Image Immunization System leveraging Invertible Neural Networks. The system, which comprises the cyber vaccinator, vaccine validator, forward pass for tamper detection, and backward pass for image self-recovery, aims to proactively immunize images against various attacks. The run-length encoding in the backward pass transforms hidden perturbations into information, facilitating the recovery of the authentic image. The middleware's expansion to multimodal content analysis, including videos and audio, provides a more comprehensive defense against digital manipulation within OSNs. These advancements reflect a commitment to robust security and holistic content integrity. The Cyber Vaccinator, using Invertible Neural Networks (INNs) for image tamper resilience and recovery, demonstrates significant effectiveness in detecting tampering and restoring images, providing a robust solution for maintaining image integrity. The Cyber Vaccinator uses an Invertible Neural Network (INN) to safeguard image integrity. It detects tampering by analyzing invariant features and responds with precise recovery methods. By continuously monitoring images, it ensures real-time tamper detection and efficient restoration, maintaining image authenticity through advanced neural network resilience and recovery techniques.
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Huang, Chen-Hsiu, and Ja-Ling Wu. "A Secure Learned Image Codec for Authenticity Verification via Self-Destructive Compression." Big Data and Cognitive Computing 9, no. 1 (January 15, 2025): 14. https://doi.org/10.3390/bdcc9010014.
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In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only after counterfeiting occurs. In this paper, we propose a novel full-resolution secure learned image codec (SLIC) designed to proactively prevent image manipulation by creating self-destructive artifacts upon re-compression. Once a sensitive image is encoded using SLIC, any subsequent re-compression or editing attempts will result in visually severe distortions, making the image’s tampering immediately evident. Because the content of an SLIC image is either original or visually damaged after tampering, images encoded with this secure codec hold greater credibility. SLIC leverages adversarial training to fine-tune a learned image codec that introduces out-of-distribution perturbations, ensuring that the first compressed image retains high quality while subsequent re-compressions degrade drastically. We analyze and compare the adversarial effects of various perceptual quality metrics combined with different learned codecs. Our experiments demonstrate that SLIC holds significant promise as a proactive defense strategy against image manipulation, offering a new approach to enhancing image credibility and authenticity in a media landscape increasingly dominated by AI-driven forgeries.
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S, Manjunatha, and Malini M. Patil. "Efficient resampling features and convolution neural network model for image forgery detection." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 183. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp183-190.
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The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.
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S, Shashikala, and Ravikumar G K. "Technology Innovation: Detection of Counterfeit Region in an Image." ECS Transactions 107, no. 1 (April 24, 2022): 4517–25. http://dx.doi.org/10.1149/10701.4517ecst.
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With availability of advanced photo editing tools today, digital images can be tampered with malicious intention at ease. The image tampering can create various ill effects like false disease diagnosis of medical images, defaming people, hiding evidences, creating false claims etc. Since images are being increasingly used as the most creditable information for inferring various conclusion and judgments in many application scenarios, it is necessary to ensure the authenticity of the image and detect any counterfeit regions in the image. Nowadays tampering can be done in sophisticated manner without leaving any trace and it becomes difficult to detect with naked eye. In this work, we survey the state of art existing works on detection of counterfeit or tampered regions in the images. The survey is done to identify the challenges in existing counterfeit detection techniques.
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Ke, Yongzhen, and Yiping Cui. "Multiple Fusion Strategies in Localization of Local Deformation Tampering." International Journal of Digital Crime and Forensics 13, no. 2 (March 2021): 103–14. http://dx.doi.org/10.4018/ijdcf.2021030107.
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Tampering with images may involve the field of crime and also bring problems such as incorrect values to the public. Image local deformation is one of the most common image tampering methods, where the original texture features and the correlation between the pixels of an image are changed. Multiple fusion strategies based on first-order difference images and their texture feature is proposed to locate the tamper in local deformation image. Firstly, texture features using overlapping blocks on one color channel are extracted and fed into fuzzy c-means clustering method to generate a tamper probability map (TPM), and then several TPMs with different block sizes are fused in the first fusion. Secondly, different TPMs with different color channels and different texture features are respectively fused in the second and third fusion. The experimental results show that the proposed method can accurately detect the location of the local deformation of an image.
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Wei, Wang, and Tang Bin. "Analysis on Feasible Methods of Digital Image Tampering Detection." Highlights in Science, Engineering and Technology 44 (April 13, 2023): 371–76. http://dx.doi.org/10.54097/hset.v44i.7386.
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In order to solve the problem of image tampering detection in the blind forensics of digital images, this study first researches and summarizes the means and methods of image tampering and forgery, and provides suggestions for methods of tampering detection using open source tools for the methods that may be used for falsification. These methods have been publicly tested by open source companies for a long time, which proves their feasibility. The methods and conclusions suggested by the research can be used in the "falsification" application of pictures in many fields, and have certain academic value and social value.
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Shikalgar, Sajeeda, Rakesh K. Yadav, and Parikshit N. Mahalle. "Lightweight MobileNet Model for Image Tempering Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (May 17, 2023): 55–69. http://dx.doi.org/10.17762/ijritcc.v11i5.6524.
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In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images.
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Khachane, Siddharth, and Deepmalya Mondal. "Image Tampering Detection using Error Level Analysis and Concatenated Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 2011–18. http://dx.doi.org/10.22214/ijraset.2023.55067.
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Abstract: Image tampering has become a leading issue in the digital age, which has given rise to serious implications in various fields such as journalism, forensics and photography. Detecting manipulated images with high accuracy is important to ensure the authenticity and credibility of visual content. In this research paper, we propose a robust and effective approach for image tampering detection utilizing a concatenated ResNet and XceptionNet model with Error Level Analysis which has achieved an accuracy of 98.58%.
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Zhang, Dengyong, Shanshan Wang, Jin Wang, Arun Kumar Sangaiah, Feng Li, and Victor S. Sheng. "Detection of Tampering by Image Resizing Using Local Tchebichef Moments." Applied Sciences 9, no. 15 (July 26, 2019): 3007. http://dx.doi.org/10.3390/app9153007.
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There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.
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Mire, Archana Vasant, Sanjay B. Dhok, Naresh J. Mistry, and Prakash D. Porey. "Tampering Localization in Double Compressed Images by Investigating Noise Quantization." International Journal of Digital Crime and Forensics 8, no. 3 (July 2016): 46–62. http://dx.doi.org/10.4018/ijdcf.2016070104.
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Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.
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Jiang, Qingyi. "Image authentication and tamper localization based on coupling between adjacent pixels." Applied and Computational Engineering 74, no. 1 (July 11, 2024): 27–39. http://dx.doi.org/10.54254/2755-2721/74/20240428.
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Digital image information has the advantages of easy storage and communication, especially with the continuous emergence of powerful image processing software, editing and modifying digital images has become extremely convenient. Subsequently, issues such as low security and easy tampering of digital images have emerged, and the integrity and authenticity of images have been questioned. Some important applications, such as news images, court evidence, medical diagnoses, etc., are not allowed to have their content modified. Passive authentication methods are often only suitable for specific images or situations, dont have the ability to locate the tamper areas. Active methods based on fragile watermarks often embed external information, making it inconvenient to perform blind authentication on the receiving end and resist malicious attacks that aim at bypassing tamper detection. In this paper, we propose to combine the advantages of passive authentication and active authentication. Firstly, an image is first divided into non-overlayed blocks, then generate check code for each pair of strongly coupled pixels within the same block. Fragile watermark technology is exploited to embed the check codes randomly based on a private key in the pixels of the image itself to achieve blind authentication for the receiver. Finally, we conduct the experiment in which a large number of images have been simulated for tampering and detected for authentication. The results show that compared with other similar methods, this paper not only has high detection accuracy, but also has high accuracy in locating the tampering location. In addition, the method proposed in this paper has other advantages in terms of computational cost and security.
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Muniappan, Ramaraj, Dhendapani Sabareeswaran, Chembath Jothish, Joe Arun Raja, Srividhya Selvaraj, Thangarasu Nainan, Bhaarathi Ilango, and Dhinakaran Sumbramanian. "Optimizing feature extraction for tampering image detection using deep learning approaches." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (September 1, 2024): 1853. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1853-1864.
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Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
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Bashir, Iram, Fawad Ahmed, Jawad Ahmad, Wadii Boulila, and Nouf Alharbi. "A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids." Entropy 21, no. 11 (November 19, 2019): 1132. http://dx.doi.org/10.3390/e21111132.
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Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.
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Siddiqui, Maha. "Blockchain-Based Secure and Efficient Secret Image Sharing with Outsourcing Computation in Wireless Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 1267–75. http://dx.doi.org/10.22214/ijraset.2024.59034.
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Abstract: We present a novel solution, the Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme, designed to enhance the security and efficiency of secret image sharing in wireless networks. Traditional Secret Image Sharing (SIS) methods generate multiple shadow images to distribute a secret image, allowing retrieval with a subset of these shadows. However, existing SIS schemes suffer from vulnerabilities, particularly during communication, where shadow images are susceptible to tampering and corruption, compromising security. Leveraging blockchain technology, BC-SESIS encrypts and stores shadow images securely within the blockchain, mitigating risks of tampering and corruption. A smart contract, empowered with identity authentication, ensures the requisite threshold (k, n) for secret image restoration. To alleviate computational burdens on smart contracts and users, we propose an efficient outsourcing computation mechanism. This method delegates the restoration task to agent miners within the encryption domain, ensuring secure execution. The effectiveness of the BC-SESIS scheme is substantiated through theoretical analysis and extensive experimentation. Results underscore its capacity to uphold communication security while delivering high computational efficiency within wireless networks.
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Singh, Uday Vikram, Suyash Rastogi, and Asim Ahmed. "Document Tampering Detection: A Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 475–78. http://dx.doi.org/10.22214/ijraset.2024.57867.
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Abstract: Document forgery techniques have evolved to create counterfeit documents nearly identical to genuine ones, evading visual detection due to advancements in printing technologies. In response, this study proposes an innovative method to uncover intrinsic device-specific characteristics concealed within counterfeit documents, focusing on original and tampered images alongside their Error Level Analysis (ELA). By leveraging seventeen diverse image quality metrics, a discriminative analysis is established to differentiate between authentic and fraudulent documents. These metrics serve as pivotal parameters, enabling the training and rigorous testing of an SVM classifier. The classifier facilitates precise identification of counterfeit documents by utilizing original and tampered images in conjunction with ELA. Preliminary experiments center on scrutinizing various documents to showcase the method's potential in accurately detecting and distinguishing between counterfeit and genuine documents..
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Naidu, Nalluri Brahma, Thokala Kavyasree, Tadikonda Ravi Teja, Pulimela Sushma Sarayu, and Sivangula Sai. "Image Forgery Detection using ResNet50." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 2222–29. http://dx.doi.org/10.22214/ijraset.2024.59317.
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Abstract: Image forgery detection is crucial in ensuring the integrity of digital media. In this study, we propose a method for detecting image tampering using Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) with a ResNet50 architecture. Leveraging the CASIA 2.0 Image Tampering Detection Dataset, which consists of authentic (Au) and tampered (Tp) images, along with metadata and annotations provided in the CASIA 2 Ground truth dataset, we develop and evaluate our model. The dataset comprises 7492 authentic images, 5125 tampered images, and 5123 files of ground truth information. ELA transformations highlight compression discrepancies, aiding in the identification of tampered regions. Our ResNet50-based CNN model, augmented with Global Average Pooling, Dense layers, and Dropout regularization, is trained using Adam optimization and binary cross-entropy loss with early stopping. Evaluation metrics, including training and validation loss/accuracy curves and confusion matrices, are used to assess model performance. The trained model is saved for future use and tested on new images to demonstrate its classification capabilities. Our approach achieves a significant level of accuracy in distinguishing between authentic and tampered images, underscoring its potential for practical image forgery detection applications and contributing to advancements in digital media forensics
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K Hebbar, Nagaveni, and Ashwini S Kunte. "TRANSFER LEARNING APPROACH FOR SPLICING AND COPY-MOVE IMAGE TAMPERING DETECTION." ICTACT Journal on Image and Video Processing 11, no. 4 (May 1, 2021): 2447–52. http://dx.doi.org/10.21917/ijivp.2021.0348.
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Image authentication before using in any security critical applications has become necessary as the image editing tools are increasing and are handy to use in today''s world. Images could be tampered in different ways, but a universal method is required to detect it. Deep learning has gained its importance because of its promising performance in many applications. In this paper a new framework for image tampering detection using Error Level Analysis (ELA) and Convolutional Neural Network (CNN) with transfer learning approach is proposed. In this method, the images are pre-processed using ELA to highlight the tampered region and are used to fine tune the entire model. Six different pre-trained models are used in the proposed framework to compare the performance in classifying the tampered and authentic images. The complexity and processing time of the proposed method is low with respect to most of the existing methods as the images are not divided into patches. The performance of the model obtained is also considerably good with an accuracy of 97.58% with Residual Network 50(ResNet50).
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Wei, Xiaoyan, Yirong Wu, Fangmin Dong, Jun Zhang, and Shuifa Sun. "Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN." Symmetry 11, no. 10 (October 1, 2019): 1223. http://dx.doi.org/10.3390/sym11101223.
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Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.
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Abdali, Natiq M., and Zahir M. Hussain. "Reference-free differential histogram-correlative detection of steganography: performance analysis." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 329. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp329-338.
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<span lang="EN-US">Recent <span>research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless </span> modified.</span>
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Li, Yewen, Wei Song, Xiaobing Zhao, Juan Wang, and Lizhi Zhao. "A Novel Image Tamper Detection and Self-Recovery Algorithm Based on Watermarking and Chaotic System." Mathematics 7, no. 10 (October 12, 2019): 955. http://dx.doi.org/10.3390/math7100955.
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With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system is proposed, and it can effectively locate the tampering region and achieve the approximate recovery of the original image by using the hidden information. The pseudo-random cyclic chain is realized by the chaotic system to construct the mapping relationship between the image subblocks. It can effectively guarantee the randomness of the positional relationship between the hidden information and the original image block for the better ergodicity of the pseudo-random chain. The recovery value optimization algorithm can represent image information better. In addition to the traditional Level-1 recovery, a weight adaptive algorithm is designed to distinguish the original block from the primary recovery block, allowing 3 × 3 neighbor block recovery to achieve better results. The experimental results show that the hierarchical tamper detection algorithm makes tamper detection have higher precision. When facing collage attacks and large general tampering, it will have higher recovery image quality and better resistance performance.
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Zhang, Jianyi, Xuanxi Huang, Yaqi Liu, Yuyang Han, and Zixiao Xiang. "GAN-based medical image small region forgery detection via a two-stage cascade framework." PLOS ONE 19, no. 1 (January 2, 2024): e0290303. http://dx.doi.org/10.1371/journal.pone.0290303.
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Using generative adversarial network (GAN) Goodfellow et al. (2014) for data enhancement of medical images is significantly helpful for many computer-aided diagnosis (CAD) tasks. A new GAN-based automated tampering attack, like CT-GAN Mirsky et al. (2019), has emerged. It can inject or remove lung cancer lesions to CT scans. Because the tampering region may even account for less than 1% of the original image, even state-of-the-art methods are challenging to detect the traces of such tampering. This paper proposes a two-stage cascade framework to detect GAN-based medical image small region forgery like CT-GAN. In the local detection stage, we train the detector network with small sub-images so that interference information in authentic regions will not affect the detector. We use depthwise separable convolution and residual networks to prevent the detector from over-fitting and enhance the ability to find forged regions through the attention mechanism. The detection results of all sub-images in the same image will be combined into a heatmap. In the global classification stage, using gray-level co-occurrence matrix (GLCM) can better extract features of the heatmap. Because the shape and size of the tampered region are uncertain, we use hyperplanes in an infinite-dimensional space for classification. Our method can classify whether a CT image has been tampered and locate the tampered position. Sufficient experiments show that our method can achieve excellent performance than the state-of-the-art detection methods.
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Shen, Jingyi, Yun Yao, and Hao Mei. "Design of Image Copy-Paste Forensics System Based on Moment Invariants." International Journal for Innovation Education and Research 9, no. 11 (November 1, 2021): 552–55. http://dx.doi.org/10.31686/ijier.vol9.iss11.3547.
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Copy-paste tampering is a common type of digital image tampering, which refers to copying a part of the image area in the same image, and then pasting it into another area of the image to generate a forged image, so as to carry out malicious operations such as fraud and framing. This kind of malicious forgery leads to the security problem of digital image. The research of digital image copy paste forensics has important theoretical significance and practical value. For digital image copy-paste tampering, this paper is based on moment invariant image copy paste tampering detection algorithm, and use Matlab software to design the corresponding tampering forensics system.
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Xu, Zijie, and Erfu Wang. "An Algorithm for Detecting and Restoring Tampered Images Using Chaotic Watermark Embedding." Electronics 13, no. 18 (September 11, 2024): 3604. http://dx.doi.org/10.3390/electronics13183604.
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In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks.
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Aminu, Ali Ahmad, and Nwojo Nnanna Agwu. "General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)." Signal & Image Processing : An International Journal 12, no. 2 (April 30, 2021): 13–32. http://dx.doi.org/10.5121/sipij.2021.12202.
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Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.
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Li, Xuejing, Qiancheng Chen, Runfu Chu, and Wei Wang. "Block mapping and dual-matrix-based watermarking for image authentication with self-recovery capability." PLOS ONE 19, no. 2 (February 2, 2024): e0297632. http://dx.doi.org/10.1371/journal.pone.0297632.
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Numerous image authentication techniques have been devised to address the potential security issue of malicious tampering with image content since digital images can be easily duplicated, modified, transformed and diffused via the Internet transmission. However, the existing works still remain many shortcomings in terms of the recovery incapability and detection accuracy with extensive tampering. To improve the performance of tamper detection and image recovery, we present a block mapping and dual-matrix-based watermarking scheme for image authentication with self-recovery capability in this paper. The to-be-embedded watermark information is composed of the authentication data and recovery data. The Authentication Feature Composition Calculation algorithm is proposed to generate the authentication data for image tamper detection and localization. Furthermore, the recovery data for tampered region recovery is comprised of self-recovery bits and mapped-recovery bits. The Set Partition in Hierarchical Trees encoding algorithm is applied to obtain the self-recovery bits, whereas the Rehashing Model-based Block Mapping algorithm is proposed to obtain the mapped-recovery bits for retrieving the damaged codes caused by tampering. Subsequently, the watermark information is embedded into the original image as digital watermarking with the guidance of a dual-matrix. The experimental results demonstrate that comparing with other state-of-the-art works, our proposed scheme not only improves the performance in recovery, but also extends the limitation of tampering rate up to 90%. Furthermore, it obtains a desirable image quality above 40 dB, large watermark payload up to 3.169 bpp, and the effective resistance to malicious attack, such as copy-move and collage attacks.
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Pandey, Prof Divya, Prof Zeba Vishwakarma, Prof Mallika Dwivedi, Jatin Pasi, and Shambhavi Pandey. "Advanced Detection of Document Tampering Using Structural Similarity Index and Image Analysis Techniques." International Journal of Multidisciplinary Research in Science, Engineering and Technology 6, no. 04 (November 25, 2023): 945–50. http://dx.doi.org/10.15680/ijmrset.2023.0604035.
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This research focuses on the application of the Structural Similarity Index (SSIM) technique for detecting tampering in various identity documents, such as PAN cards, Aadhar cards, and voter IDs. The SSIM method is employed to assess the structural similarity between the original and provided document images. Additionally, grayscale conversion and thresholding techniques are utilized to analyze shapes and contours, further aiding in the identification of tampered areas. Experimental results show that a low SSIM score indicates potential tampering in the provided image. Visualizations, including contour overlays, difference maps, and threshold-based comparisons, enhance the clarity of differences between original and tampered document images.
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Rao, Jyoti, Sarika Jankar, and Ashwini Jarali. "Image Tampering Detection and Repairing." International Journal of Computer Applications 85, no. 17 (January 16, 2014): 11–15. http://dx.doi.org/10.5120/14932-3468.
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Lago, Federica, Quoc-Tin Phan, and Giulia Boato. "Visual and Textual Analysis for Image Trustworthiness Assessment within Online News." Security and Communication Networks 2019 (April 14, 2019): 1–14. http://dx.doi.org/10.1155/2019/9236910.
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The majority of news published online presents one or more images or videos, which make the news more easily consumed and therefore more attractive to huge audiences. As a consequence, news with catchy multimedia content can be spread and get viral extremely quickly. Unfortunately, the availability and sophistication of photo editing software are erasing the line between pristine and manipulated content. Given that images have the power of bias and influence the opinion and behavior of readers, the need of automatic techniques to assess the authenticity of images is straightforward. This paper aims at detecting images published within online news that have either been maliciously modified or that do not represent accurately the event the news is mentioning. The proposed approach composes image forensic algorithms for detecting image tampering, and textual analysis as a verifier of images that are misaligned to textual content. Furthermore, textual analysis can be considered as a complementary source of information supporting image forensics techniques when they falsely detect or falsely ignore image tampering due to heavy image postprocessing. The devised method is tested on three datasets. The performance on the first two shows interesting results, with F1-score generally higher than 75%. The third dataset has an exploratory intent; in fact, although showing that the methodology is not ready for completely unsupervised scenarios, it is possible to investigate possible problems and controversial cases that might arise in real-world scenarios.
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Abu-Faraj, Mua’ad M., and Ziad A. Alqadi. "Image Encryption using Variable Length Blocks and Variable Length PK." International Journal of Computer Science and Mobile Computing 11, no. 3 (March 30, 2022): 138–51. http://dx.doi.org/10.47760/ijcsmc.2022.v11i03.016.
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The colored digital image is one of the most important and popular types of digital data for use in many vital applications, which requires the provision of safe methods to protect it from penetration operations and protect it from tampering and data thieves. In this research paper, a new method for protecting digital images of various types will be presented, which is characterized by ease of implementation and providing a high degree of security and protection for the digital image. A secret color image known only by the sender and receiver will be used as an image_ key, this image_key will be used to generate a private key to encrypt-decrypted any color image by applying image resizing. The private key will be variable, and will match the image block size. The image to be encrypted-decrypted will be divided into blocks, the block size will be variable and agree upon between the sender and receiver. The proposed method will be implemented, the obtained results will be analyzed to prove the efficiency, security level and quality parameters provided by the proposed method.
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Oyama, Tatsuya, Manami Hagizaki, Shunsuke Okura, and Takeshi Fujino. "Implementation of an Image Tampering Detection System with a CMOS Image Sensor PUF and OP-TEE." Sensors 24, no. 22 (November 5, 2024): 7121. http://dx.doi.org/10.3390/s24227121.
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Since image recognition systems use image data acquired by image sensors for analysis by AI technology, an important security issue is guaranteeing the authenticity of data transmitted from image sensors to successfully perform inference using AI. There have been reports of physical attacks on image sensor interfaces by tampering with images to cause misclassifications in AI classification results. As a countermeasure against these attacks, it is effective to add authenticity to image data with a message authentication code (MAC). For the implementation of this, it is important to have technologies for generating MAC keys on image sensors and to create an environment for secure MAC verification on the host device. For MAC key generation, we used the CIS-PUF technology, which generates MAC keys from PUF responses and random numbers from CMOS image sensor variations. For the secure MAC verification, we used TEE technology, which executes security-critical processes in an environment isolated from the normal operating system. In this study, we propose and demonstrate an image tampering detection system based on MAC verification with CIS-PUF and OP-TEE in an open portable TEE on an ARM processor. In the experiments, we demonstrated a system that computed and transmitted MAC for captured images using the CIS-PUF key and then performed MAC verification in the secure world of the OP-TEE.
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Shivanandappa, Manjunatha, and Malini M. Patil. "Extraction of image resampling using correlation aware convolution neural networks for image tampering detection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 3033. http://dx.doi.org/10.11591/ijece.v12i3.pp3033-3043.
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<span>Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.</span>
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Shabat, Hafedh Ali, Khamael Raqim Raheem, and Wafaa Mohammed Ridha Shakir. "Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density." Journal of Image and Graphics 12, no. 1 (2024): 10–15. http://dx.doi.org/10.18178/joig.12.1.10-15.
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Recent research has demonstrated the success of employing neural networks for the purpose of detecting image tampering. Nevertheless, the utilization of reference-free steganalysis has become increasingly popular as a result of the challenges associated with obtaining an annotated dataset. This dataset is crucial for the classification process using neural networks, which aims to detect and identify instances of tampering. This paper introduces a robust approach to blind steganalysis, utilizing image spectral density and differential histogram correlative power spectral density. The proposed method employed two distinct forms of image data, namely a gray-scale image and true-color image data. The results indicate that the proposed methodology successfully achieved the anticipated outcomes in identifying manipulated images as evidenced by its successful application on the two distinct datasets. In the experiment results, the proposed technique succeeded quite well in terms of accuracy at low embedding ratios. Also, it successfully recognized sequential and random least significant bit steganography.
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Nathalie Diane, Wandji Nanda, Sun Xingming, and Fah Kue Moise. "A Survey of Partition-Based Techniques for Copy-Move Forgery Detection." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/975456.
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A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques.
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Niu, Dongmei, Hongxia Wang, Minquan Cheng, and Canghong Shi. "Reference Sharing Mechanism-Based Self-Embedding Watermarking Scheme with Deterministic Content Reconstruction." Security and Communication Networks 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/2516324.
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This paper presents a reference sharing mechanism-based self-embedding watermarking scheme. The host image is embedded with watermark bits including the reference data for content recovery and the authentication data for tampering location. The special encoding matrix derived from the generator matrix of selected systematic Maximum Distance Separable (MDS) code is adopted. The reference data is generated by encoding all the representative data of the original image blocks. On the receiver side, the tampered image blocks can be located by the authentication data. The reference data embedded in one image block can be shared by all the image blocks to restore the tampered content. The tampering coincidence problem can be avoided at the extreme. The maximal tampering rate is deduced theoretically. Experimental results show that, as long as the tampering rate is less than the maximal tampering rate, the content recovery is deterministic. The quality of recovered content does not decrease with the maximal tampering rate.
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T. Blessington, Dr Praveen, and Prof Ravindra Mule. "Image Forgery Detection Based on Parallel Convolutional Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 30, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28428.
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Abstract— Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different preprocessing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning for pre-trained ImageNet(via-fine-tuning) and implement it over our dataset CASIA V2.0. So, it covers the pre-processing techniques with basic CNN model and later see the powerful effect of the transfer learning models. Keywords— image tampering, convolution neural network (CNN), error level analysis (ELA), transfer learning, sharpening filter, fine-tuning