Journal articles: 'Splicing detection'

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Peng, Pengfei, Guoqing Liang, and Tao Luan. "Multi-View Inconsistency Analysis for Video Object-Level Splicing Localization." International Journal of Emerging Technologies and Advanced Applications 1, no. 3 (April 24, 2024): 1–5. http://dx.doi.org/10.62677/ijetaa.2403111.

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In the digital era, the widespread use of video content has led to the rapid development of video editing technologies. However, it has also raised concerns about the authenticity and integrity of multimedia content. Video splicing forgery has emerged as a challenging and deceptive technique used to create fake video objects, potentially for malicious purposes such as deception, defamation, and fraud. Therefore, the detection of video splicing forgery has become critically important. Nevertheless, due to the complexity of video data and a lack of relevant datasets, research on video splicing forgery detection remains relatively limited. This paper introduces a novel method for detecting video object splicing forgery, which enhances detection performance by deeply exploring inconsistent features between different source videos. We incorporate various feature types, including edge luminance, texture, and video quality information, and utilize a joint learning approach with Convolutional Neural Network (CNN) and Vision Transformer (ViT) models. Experimental results demonstrate that our method excels in detecting video object splicing forgery, offering promising prospects for further advancements in this field.

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Šetrajčič Dragoš, Vita, Vida Stegel, Ana Blatnik, Gašper Klančar, Mateja Krajc, and Srdjan Novaković. "New Approach for Detection of Normal Alternative Splicing Events and Aberrant Spliceogenic Transcripts with Long-Range PCR and Deep RNA Sequencing." Biology 10, no. 8 (July 23, 2021): 706. http://dx.doi.org/10.3390/biology10080706.

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RNA sequencing is a promising technique for detecting normal and aberrant RNA isoforms. Here, we present a new single-gene, straightforward 1-day hands-on protocol for detection of splicing alterations with deep RNA sequencing from blood. We have validated our method’s accuracy by detecting previously published normal splicing isoforms of STK11 gene. Additionally, the same technique was used to provide the first comprehensive catalogue of naturally occurring alternative splicing events of the NBN gene in blood. Furthermore, we demonstrate that our approach can be used for detection of splicing impairment caused by genetic variants. Therefore, we were able to reclassify three variants of uncertain significance: NBN:c.584G>A, STK11:c.863-5_863-3delCTC and STK11:c.615G>A. Due to the simplicity of our approach, it can be incorporated into any molecular diagnostics laboratory for determination of variant’s impact on splicing.

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Yan, Diqun, Mingyu Dong, and Jinxing Gao. "Exposing Speech Transsplicing Forgery with Noise Level Inconsistency." Security and Communication Networks 2021 (January 27, 2021): 1–6. http://dx.doi.org/10.1155/2021/6659371.

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Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.

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Ramirez-Rodriguez, Ana Elena, Rodrigo Eduardo Arevalo-Ancona, Hector Perez-Meana, Manuel Cedillo-Hernandez, and Mariko Nakano-Miyatake. "AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks." Applied Sciences 14, no. 13 (June 26, 2024): 5545. http://dx.doi.org/10.3390/app14135545.

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The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.

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Srinivasan, Karpagam, Lily Shiue, Justin D. Hayes, Ross Centers, Sean Fitzwater, Rebecca Loewen, Lillian R. Edmondson, Jessica Bryant, Michael Smith, and Claire Rommelfanger. "Detection and measurement of alternative splicing using splicing-sensitive microarrays." Methods 37, no. 4 (December 2005): 345–59. http://dx.doi.org/10.1016/j.ymeth.2005.09.007.

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Alshwely, Mohammed Kassem, and Saad N. AlSaad. "Image splicing detection based on noise level approach." Al-Mustansiriyah Journal of Science 31, no. 4 (December 20, 2020): 55. http://dx.doi.org/10.23851/mjs.v31i4.899.

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The rapid development in technology and the spread of editing image software has led to spread forgery in digital media. It is now not easy by just looking at an image to know whether the image is original or has been tampered. This article describes a new image splicing detection method based on noise level as a major feature to detect the tempered region. Principal Component Analysis (PCA) is exploited to estimate the noise of image and the K-means clustering for authentic and forged region classification. The proposed method adopts Columbia Uncompressed Image Splicing Dataset for evaluation and effectiveness. The experimental results for 360 images demonstrate that the method achieved an 83.33% for detecting tampered region this percentage represent a promising result competed with Stat-of-art splicing detection methods.

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Kadam, Kalyani Dhananjay, Swati Ahirrao, and Ketan Kotecha. "Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing." Data 6, no. 10 (September 28, 2021): 102. http://dx.doi.org/10.3390/data6100102.

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Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Carvalho, and CASIA V1.0. However, these datasets are employed for the detection of image splicing. There are also a few custom datasets available such as Modified CASIA, AbhAS, which are also employed for the detection of image splicing forgeries. A study of existing datasets used for the detection of image splicing reveals that they are limited to only image splicing and do not contain multiple spliced images. This research work presents a Multiple Image Splicing Dataset, which consists of a total of 300 multiple spliced images. We are the pioneer in developing the first publicly available Multiple Image Splicing Dataset containing high-quality, annotated, realistic multiple spliced images. In addition, we are providing a ground truth mask for these images. This dataset will open up opportunities for researchers working in this significant area.

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Su, Hang. "Image splicing detection using integrated LBP and DCT features." Applied and Computational Engineering 101, no. 1 (November 8, 2024): 71–78. http://dx.doi.org/10.54254/2755-2721/101/20240976.

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Abstract. Image splicing is one of the most common techniques used for picture manipulation and forgery. With the advent of user-friendly photo editing software, image splicing has become more prevalent and increasingly difficult to detect. This paper proposes a passive photo splicing detection approach based on Local Binary Patterns (LBP) and Discrete Cosine Transform (DCT) to identify splicing forgeries. The input RGB images are first converted to the YCbCr color space. Subsequently, the chrominance channels, Cb and Cr, are divided into overlapping blocks. Each block's LBP code is then transformed into the DCT domain. For each block, the standard deviation of each DCT coefficient is computed and used as a feature. Support Vector Machine (SVM) is employed as the classifier in a predictive model to determine whether the images have been spliced. To evaluate the proposed approach, two benchmark datasets for photo tampering were utilized. Experimental results indicate that the proposed method outperforms traditional splicing detection techniques in terms of detection accuracy and performance. This enhanced detection capability underscores the potential of combining LBP and DCT features with SVM classification for robust image splicing detection, paving the way for improved digital forensics tools in combating image manipulation.

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Anna, Abramowicz, and Gos Monika. "Splicing mutations in human genetic disorders: examples, detection, and confirmation." Journal of Applied Genetics 59, no. 3 (April 21, 2018): 253–68. http://dx.doi.org/10.1007/s13353-018-0444-7.

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Abstract Precise pre-mRNA splicing, essential for appropriate protein translation, depends on the presence of consensus “cis” sequences that define exon-intron boundaries and regulatory sequences recognized by splicing machinery. Point mutations at these consensus sequences can cause improper exon and intron recognition and may result in the formation of an aberrant transcript of the mutated gene. The splicing mutation may occur in both introns and exons and disrupt existing splice sites or splicing regulatory sequences (intronic and exonic splicing silencers and enhancers), create new ones, or activate the cryptic ones. Usually such mutations result in errors during the splicing process and may lead to improper intron removal and thus cause alterations of the open reading frame. Recent research has underlined the abundance and importance of splicing mutations in the etiology of inherited diseases. The application of modern techniques allowed to identify synonymous and nonsynonymous variants as well as deep intronic mutations that affected pre-mRNA splicing. The bioinformatic algorithms can be applied as a tool to assess the possible effect of the identified changes. However, it should be underlined that the results of such tests are only predictive, and the exact effect of the specific mutation should be verified in functional studies. This article summarizes the current knowledge about the “splicing mutations” and methods that help to identify such changes in clinical diagnosis.

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Li, Qian, Rangding Wang, and Dawen Xu. "A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise." Electronics 12, no. 6 (March 13, 2023): 1362. http://dx.doi.org/10.3390/electronics12061362.

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Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.

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Liu, Jin, Hefei Ling, Fuhao Zou, WeiQi Yan, and Zhengding Lu. "Digital Image Forensics Using Multi-Resolution Histograms." International Journal of Digital Crime and Forensics 2, no. 4 (October 2010): 37–50. http://dx.doi.org/10.4018/jdcf.2010100103.

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In this paper, the authors investigate the prospect of using multi-resolution histograms (MRH) in conjunction with digital image forensics, particularly in the detection of two kinds of copy-move manipulations, i.e., cloning and splicing. To the best of the authors’ knowledge, this is the first work that uses the same feature in both cloning and splicing forensics. The experimental results show the simplicity and efficiency of using MRH for the purpose of clone detection and splicing detection.

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Milani, Lili, Mona Fredriksson, and Ann-Christine Syvänen. "Detection of Alternatively Spliced Transcripts in Leukemia Cell Lines by Minisequencing on Microarrays." Clinical Chemistry 52, no. 2 (February 1, 2006): 202–11. http://dx.doi.org/10.1373/clinchem.2005.062042.

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Abstract Background: Recent genome-wide expression studies suggest that ∼80% of the 25 000 human genes undergo alternative splicing. Alternative splicing may be associated with human diseases, particularly with cancer, but the molecular disease mechanisms are poorly understood. Convenient, novel methods for multiplexed detection of alternatively spliced transcripts are needed. Methods: We devised a new approach for detecting splice variants based on a tag-microarray minisequencing system, originally developed for genotyping single-nucleotide polymorphisms. We established the system for multiplexed detection of 61 alternatively spliced transcripts in a panel of 19 cancer-related genes and used it to dissect the splicing patterns in cancer and endothelial cells. Results: Our microarray system detected 82% of the splice variants screened for, including both simple and complex splice variants, in at least 1 of the leukemia cell types analyzed. The intraassay CV values for our method ranged from 0.01 to 0.34 (mean, 0.13) for 5 replicate measurements. Our system allowed semiquantitative comparison of the splicing patterns between the cell lines. Similar, but not identical, patterns of alternative splicing were observed among the leukemia cell lines. Size analysis of the PCR products subjected to the tag-array minisequencing system and real-time PCR with exon-junction probes verified the results from the microarray system. Conclusions: The microarray-based method is a robust and easily accessible tool for parallel detection of alternatively spliced transcripts of multiple genes. It can be used for studying alternative splicing in cancer progression and for following up drug treatment, and it may be a useful tool in clinical diagnostics for cancer and other disorders.

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Burvin, P. Sabeena, and J. Monica Esther. "Analysis of Digital Image Splicing Detection." IOSR Journal of Computer Engineering 16, no. 2 (2014): 10–13. http://dx.doi.org/10.9790/0661-162111013.

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Jun Hou, Haojie Shi, and Yan Cheng. "Image Splicing Detection by Border Features." International Journal of Advancements in Computing Technology 5, no. 9 (May 31, 2013): 857–63. http://dx.doi.org/10.4156/ijact.vol5.issue9.102.

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Ahmed, Belal, T. Aaron Gulliver, and Saif alZahir. "Image splicing detection using mask-RCNN." Signal, Image and Video Processing 14, no. 5 (January 17, 2020): 1035–42. http://dx.doi.org/10.1007/s11760-020-01636-0.

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Kobozieva, А. А., and B. G. Yenakiiev. "Method for image splicing forgery detection." Informatics and mathematical methods in simulation 14, no. 1-2 (April 2, 2024): 24–36. http://dx.doi.org/10.15276/imms.v14.no1-2.24.

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Fahmi, Naima Ahmed, Heba Nassereddeen, Jaewoong Chang, Meeyeon Park, Hsinsung Yeh, Jiao Sun, Deliang Fan, Jeongsik Yong, and Wei Zhang. "AS-Quant: Detection and Visualization of Alternative Splicing Events with RNA-seq Data." International Journal of Molecular Sciences 22, no. 9 (April 25, 2021): 4468. http://dx.doi.org/10.3390/ijms22094468.

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(1) Background: A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of the proteome and is critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. (2) Results: We propose AS-Quant, (Alternative Splicing Quantitation), a robust program to identify alternative splicing events from RNA-seq data. We then extended AS-Quant to visualize the splicing events with short-read coverage plots along with complete gene annotation. The tool works in three major steps: (i) calculate the read coverage of the potential spliced exons and the corresponding gene; (ii) categorize the events into five different categories according to the annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot for user specified splicing events. Our extensive experiments on simulated and real datasets demonstrate that AS-Quant outperforms the other three widely used baselines, SUPPA2, rMATS, and diffSplice for detecting alternative splicing events. Moreover, the significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR experiment. (3) Availability: AS-Quant is implemented in Python 3.0. Source code and a comprehensive user’s manual are freely available online.

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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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Horinouchi, Tomoko, Kandai Nozu, Tomohiko Yamamura, Shogo Minamikawa, Takashi Omori, Keita Nakanishi, Junya Fujimura, et al. "Detection of Splicing Abnormalities and Genotype-Phenotype Correlation in X-linked Alport Syndrome." Journal of the American Society of Nephrology 29, no. 8 (June 29, 2018): 2244–54. http://dx.doi.org/10.1681/asn.2018030228.

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BackgroundX-linked Alport syndrome (XLAS) is a progressive hereditary nephropathy caused by mutations in the COL4A5 gene. Genotype-phenotype correlation in male XLAS is relatively well established; relative to truncating mutations, nontruncating mutations exhibit milder phenotypes. However, transcript comparison between XLAS cases with splicing abnormalities that result in a premature stop codon and those with nontruncating splicing abnormalities has not been reported, mainly because transcript analysis is not routinely conducted in patients with XLAS.MethodsWe examined transcript expression for all patients with suspected splicing abnormalities who were treated at one hospital between January of 2006 and July of 2017. Additionally, we recruited 46 males from 29 families with splicing abnormalities to examine genotype-phenotype correlation in patients with truncating (n=21, from 14 families) and nontruncating (n=25, from 15 families) mutations at the transcript level.ResultsWe detected 41 XLAS families with abnormal splicing patterns and described novel XLAS atypical splicing patterns (n=14) other than exon skipping caused by point mutations in the splice consensus sequence. The median age for developing ESRD was 20 years (95% confidence interval, 14 to 23 years) among patients with truncating mutations and 29 years (95% confidence interval, 25 to 40 years) among patients with nontruncating mutations (P=0.001).ConclusionsWe report unpredictable atypical splicing in the COL4A5 gene in male patients with XLAS and reveal that renal prognosis differs significantly for patients with truncating versus nontruncating splicing abnormalities. Our results suggest that splicing modulation should be explored as a therapy for XLAS with truncating mutations.

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Kumazaki, T., Y. Mitsui, K. Hamada, H. Sumida, and M. Nishiyama. "Detection of alternative splicing of fibronectin mRNA in a single cell." Journal of Cell Science 112, no. 10 (May 15, 1999): 1449–53. http://dx.doi.org/10.1242/jcs.112.10.1449.

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Pre-fibronectin mRNA is subject to alternative splicing at three sites, EDA, EDB and IIICS. We analyzed the alternative splicing of fibronectin mRNA in a single cell. Reverse transcription-polymerase chain reaction analyses showed cells that produced a single form of mRNA at each one of these sites as well as cells that produced multiple forms at a given site: for example, some cells produced either the EDA(+) or EDA(-) form of the mRNA and other cells produced both forms. About 80% of the cells produced both (+) and (-) forms of the mRNA at the EDA and EDB sites, and the remaining cells contained either the (+) or (-) form. Five forms of fibronectin mRNA can result from alternative splicing at the IIICS site. Complex combinations of alternative splicing products were observed among the individual cells: there were ten different combinations of mRNA isoforms with respect to the IIICS site. Statistically significant changes in alternative splicing at the IIICS site were observed during cellular senescence.

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Weir, Jonathan, Raymond Lau, and WeiQi Yan. "Digital Image Splicing Using Edges." International Journal of Digital Crime and Forensics 2, no. 4 (October 2010): 63–75. http://dx.doi.org/10.4018/jdcf.2010100105.

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In this paper, the authors splice together an image which has been split up on a piece of paper by using duplication detection. The nearest pieces are connected using edge searching and matching and the pieces that have graphics or textures are matched using the edge shape and intersection between the two near pieces. Thus, the initial step is to mark the direction of each piece and put the pieces that have straight edges to the initial position to determine the profile of the whole image. The other image pieces are then fixed into the corresponding position by using the edge information, i.e., shape, residual trace and matching, after duplication or sub-duplication detection. In the following steps, the patches with different edge shapes are searched using edge duplication detection. With the reduction of rest pieces, the montage procedure will become easier and faster.

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Hussien, Nadheer Younus, Rasha O. Mahmoud, and Hala Helmi Zayed. "Deep Learning on Digital Image Splicing Detection Using CFA Artifacts." International Journal of Sociotechnology and Knowledge Development 12, no. 2 (April 2020): 31–44. http://dx.doi.org/10.4018/ijskd.2020040102.

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Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

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Haoting Liu, Haoting Liu, Wei Wang Wei Wang, Xinfeng Li Xinfeng Li, and Feng Gao Feng Gao. "Detection of automatic abnormity in the winding and splicing of fiber-optic coil." Chinese Optics Letters 11, no. 10 (2013): 101501–4. http://dx.doi.org/10.3788/col201311.101501.

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Ahir, Prof D., Sakshi Kapse, Sayali Mhaske, Tanuja Pansare, and Param Kalane. "Adversarial Learning for Constrained Image Splicing Detection and Localization." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 4320–27. http://dx.doi.org/10.22214/ijraset.2024.62616.

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Abstract: In the era of digital media, the manipulation of images has become a significant concern, particularly on social media platforms. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to develop a robust system capable of recognizing and localizing spliced or tampered images. This research proposes a novel approach that leverages Convolutional Neural Networks (CNNs) and adversarial learningtechniques to enhance the detection and localization of image splicing. The system is designed to aid in distinguishing between authentic and manipulated images, thereby promoting transparency and trust in digital media. With the rapid advancement of digital media and image editingtools, the manipulation of visual content has become increasingly prevalent, posing significant challenges to the credibility and trustworthiness of images shared on social media platforms. One pervasive form of image manipulation is image splicing, where portions of two or more images are seamlessly combined to create a composite image, often with the intent to mislead or deceive viewers. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to address this issue by developing a robust system capable of accurately detecting and localizing spliced regions within tampered images. This research proposes a novel approach that synergistically integrates Convolutional Neural Networks (CNNs) and adversarial learning techniques to enhance the performance of image splicing detection and localization. The proposed system employs a two-stage process: first, a CNN-based classifier is trained on a large dataset of authentic and spliced images to learn discriminative features for distinguishing between the two classes; subsequently, an adversarial learning model is employed to generate adversarial examples that can deceive the CNN classifier, while the classifier is iteratively updated to become more robust against these adversarial perturbations. By accurately identifying and localizing spliced regions within images, this research contributes to promoting transparency and trust in digital media shared on social media platforms, ultimately empowering users to make informed decisions about the authenticity of visual content. The proposed system has potential applications in various domains, including journalism, law enforcement, and content moderation, where verifying the integrity of visual evidence is of paramount importance.

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Polosoro, Aqwin, Wening Enggarini, Kusumawaty Kusumanegara, Toto Hadiarto, Miftahudin Miftahudin, and Ence Darmo Jaya Supena. "Detection and quantification of splicing variants of Hd3a gene in oil palm." Indonesian Journal of Biotechnology 29, no. 1 (March 30, 2024): 40. http://dx.doi.org/10.22146/ijbiotech.88327.

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Alternative splicing is a complex process that contributes to the generation of diverse mRNA and protein isoforms, including in oil palm (Elaeis guineensis). Despite their importance, many functions of alternative splicing genes remain poorly characterized. This study aims to investigate splicing variants of gene encoding Heading date 3a in E. guineensis (EgHd3a) using the GenBank database and ClustalW algorithm. To ensure the data accuracy and reliability of design isoform‐ specific primers, special emphasis is given to primer design techniques and validation using polymerase chain reaction (PCR) and quantitative real‐time (qRT)‐PCR analysis. The designed primers demonstrated high specificity and discrimination between mRNA specimens. Nucleotide variations at the 3’‐end influenced the specificity of primers with the addition of GC composition. Furthermore, qRT‐PCR analysis revealed a strong correlation between Ct values and gene concentration for the isoforms which indicates a reliable amplification of EgHd3a. Although two isoforms, Hd3a‐X2 and Hd3a‐X3, showed slightly higher than acceptable PCR efficiency values, caution is advised to prevent non‐specific amplification. Despite the challenge posed by the limitation of primer positioning due to alternative splicing, the chosen primer proved optimal for analysis. This study highlights the importance of considering alternative splicing in gene quantification experiments and provides insights into the critical steps, methods, and quality control measures necessary for accurately detecting alternative splicing events, contributing to understanding this complex biological process.

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Hu, Jianhua, Xuming He, Gilbert J. Cote, and Ralf Krahe. "Singular Value Decomposition–Based Alternative Splicing Detection." Journal of the American Statistical Association 104, no. 487 (September 2009): 944–53. http://dx.doi.org/10.1198/jasa.2009.ap08283.

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Barash, Yoseph, Benjamin J. Blencowe, and Brendan J. Frey. "Model-based detection of alternative splicing signals." Bioinformatics 26, no. 12 (June 1, 2010): i325—i333. http://dx.doi.org/10.1093/bioinformatics/btq200.

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Ling, Zhang, Mu Wenpeng, and Chen Beijing. "Adaptive residual algorithm for image splicing detection." Journal of Image and Graphics 29, no. 2 (2024): 419–29. http://dx.doi.org/10.11834/jig.230098.

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Zhang, Yulin, Hongxia Wang, Rui Zhang, and Jingyuan Zhang. "Semantic consistency-relevant multitask splicing-tampered detection." Journal of Image and Graphics 28, no. 3 (2023): 775–88. http://dx.doi.org/10.11834/jig.220549.

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Jenkinson, Garrett, Yang I. Li, Shubham Basu, Margot A. Cousin, Gavin R. Oliver, and Eric W. Klee. "LeafCutterMD: an algorithm for outlier splicing detection in rare diseases." Bioinformatics 36, no. 17 (April 21, 2020): 4609–15. http://dx.doi.org/10.1093/bioinformatics/btaa259.

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Abstract Motivation Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. Results We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. Availability and implementation The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. Supplementary information Supplementary data are available at Bioinformatics online.

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Xiang, Chengzhi, and Ailin Liang. "Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm." Atmosphere 13, no. 1 (January 10, 2022): 109. http://dx.doi.org/10.3390/atmos13010109.

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In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal.

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Jalab, Hamid, Thamarai Subramaniam, Rabha Ibrahim, Hasan Kahtan, and Nurul Noor. "New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection." Entropy 21, no. 4 (April 5, 2019): 371. http://dx.doi.org/10.3390/e21040371.

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Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

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Hoffmann, Steve, Christian Otto, Gero Doose, Andrea Tanzer, David Langenberger, Sabina Christ, Manfred Kunz, et al. "A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection." Genome Biology 15, no. 2 (2014): R34. http://dx.doi.org/10.1186/gb-2014-15-2-r34.

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Tosi, Mario, Stefan Stamm, and Diana Baralle. "RNA splicing meets genetic testing: detection and interpretation of splicing defects in genetic diseases." European Journal of Human Genetics 18, no. 6 (February 24, 2010): 737–38. http://dx.doi.org/10.1038/ejhg.2010.18.

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Zheng, Weibo, Jing Chen, Thomas G. Doak, Weibo Song, and Ying Yan. "ADFinder: accurate detection of programmed DNA elimination using NGS high-throughput sequencing data." Bioinformatics 36, no. 12 (April 4, 2020): 3632–36. http://dx.doi.org/10.1093/bioinformatics/btaa226.

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Abstract Motivation Programmed DNA elimination (PDE) plays a crucial role in the transitions between germline and somatic genomes in diverse organisms ranging from unicellular ciliates to multicellular nematodes. However, software specific for the detection of DNA splicing events is scarce. In this paper, we describe Accurate Deletion Finder (ADFinder), an efficient detector of PDEs using high-throughput sequencing data. ADFinder can predict PDEs with relatively low sequencing coverage, detect multiple alternative splicing forms in the same genomic location and calculate the frequency for each splicing event. This software will facilitate research of PDEs and all down-stream analyses. Results By analyzing genome-wide DNA splicing events in two micronuclear genomes of Oxytricha trifallax and Tetrahymena thermophila, we prove that ADFinder is effective in predicting large scale PDEs. Availability and implementation The source codes and manual of ADFinder are available in our GitHub website: https://github.com/weibozheng/ADFinder. Supplementary information Supplementary data are available at Bioinformatics online.

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Subramaniam, Jalab, Ibrahim, and Mohd Noor. "Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients." Symmetry 11, no. 11 (November 10, 2019): 1392. http://dx.doi.org/10.3390/sym11111392.

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The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

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Zhang, Jiabao, Shenghua Liu, Wenting Hou, Siddharth Bhatia, Huawei Shen, Wenjian Yu, and Xueqi Cheng. "AugSplicing: Synchronized Behavior Detection in Streaming Tensors." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4653–61. http://dx.doi.org/10.1609/aaai.v35i5.16595.

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How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e.~subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AUGSPLICING, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AUGSPLICING is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dense block detection; (3) streaming and faster than the existing streaming algorithm, with closely comparable accuracy.

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Bentata, Mercedes, Guy Morgenstern, Yuval Nevo, Gillian Kay, Avital Granit Mizrahi, Mark Temper, Ofra Maimon, et al. "Splicing Factor Transcript Abundance in Saliva as a Diagnostic Tool for Breast Cancer." Genes 11, no. 8 (August 3, 2020): 880. http://dx.doi.org/10.3390/genes11080880.

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Breast cancer is the second leading cause of death in women above 60 years in the US. Screening mammography is recommended for women above 50 years; however, 22% of breast cancer cases are diagnosed in women below this age. We set out to develop a test based on the detection of cell-free RNA from saliva. To this end, we sequenced RNA from a pool of ten women. The 1254 transcripts identified were enriched for genes with an annotation of alternative pre-mRNA splicing. Pre-mRNA splicing is a tightly regulated process and its misregulation in cancer cells promotes the formation of cancer-driving isoforms. For these reasons, we chose to focus on splicing factors as biomarkers for the early detection of breast cancer. We found that the level of the splicing factors is unique to each woman and consistent in the same woman at different time points. Next, we extracted RNA from 36 healthy subjects and 31 breast cancer patients. Recording the mRNA level of seven splicing factors in these samples demonstrated that the combination of all these factors is different in the two groups (p value = 0.005). Our results demonstrate a differential abundance of splicing factor mRNA in the saliva of breast cancer patients.

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Xu, Bo, Guangjie Liu, and Yuewei Dai. "Detecting Image Splicing Using Merged Features in Chroma Space." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/262356.

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Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature.

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Su, Taojunfeng, Michael A. R. Hollas, Ryan T. Fellers, and Neil L. Kelleher. "Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics." Annual Review of Biomedical Data Science 6, no. 1 (August 10, 2023): 357–76. http://dx.doi.org/10.1146/annurev-biodatasci-020722-044021.

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Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

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张, 喆葳. "Image Splicing Detection Based on Image Quality Metrics." Journal of Image and Signal Processing 07, no. 03 (2018): 128–35. http://dx.doi.org/10.12677/jisp.2018.73015.

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Phang, Tzulip. "R and Bioconductor solutions for alternative splicing detection." Human Genomics 4, no. 2 (2009): 131. http://dx.doi.org/10.1186/1479-7364-4-2-131.

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Freistadt, Marion S. "Detection of a possibletrans-splicing intermediate inTrypanosoma brucei." Nucleic Acids Research 16, no. 15 (1988): 7720. http://dx.doi.org/10.1093/nar/16.15.7720.

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Aschoff, Moritz, Agnes Hotz-Wagenblatt, Karl-Heinz Glatting, Matthias Fischer, Roland Eils, and Rainer König. "SplicingCompass: differential splicing detection using RNA-Seq data." Bioinformatics 29, no. 9 (February 28, 2013): 1141–48. http://dx.doi.org/10.1093/bioinformatics/btt101.

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Zhao, Hong, Yifan Chen, Rui Wang, and Hafiz Malik. "Audio splicing detection and localization using environmental signature." Multimedia Tools and Applications 76, no. 12 (July 26, 2016): 13897–927. http://dx.doi.org/10.1007/s11042-016-3758-7.

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Zhu, Nan, and Zhao Li. "Blind image splicing detection via noise level function." Signal Processing: Image Communication 68 (October 2018): 181–92. http://dx.doi.org/10.1016/j.image.2018.07.012.

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Baleanu, Dumitru, Ahmad Sami Al-Shamayleh, and Rabha W. Ibrahim. "Image Splicing Detection Using Generalized Whittaker Function Descriptor." Computers, Materials & Continua 75, no. 2 (2023): 3465–77. http://dx.doi.org/10.32604/cmc.2023.037162.

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Edelman, E. Jennifer, Yelena Maksimova, Feride Duru, Cigdem Altay, and Patrick G. Gallagher. "A complex splicing defect associated with homozygous ankyrin-deficient hereditary spherocytosis." Blood 109, no. 12 (June 15, 2007): 5491–93. http://dx.doi.org/10.1182/blood-2006-09-046573.

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Abstract Defects in erythrocyte ankyrin are the most common cause of typical, dominant hereditary spherocytosis (HS). Detection of ankyrin gene mutations has been complicated by allelic heterogeneity, large gene size, frequent de novo mutations, and associated mRNA instability. Using denaturing high-performance liquid chromatography (DHPLC)–based mutation detection, a mutation in the splice acceptor of exon 17 was discovered in a Turkish family. Reticulocyte RNA and functional minigene splicing assays in heterologous cells revealed that this mutation was associated with a complex pattern of aberrant splicing, suggesting that removal of intron 16 is important for ordered ankyrin mRNA splicing. As predicted by clinical, laboratory, and biochemical studies, the parents were heterozygous and the proband was homozygous for this mutation. These data indicate that DHPLC offers a highly sensitive, economic, and rapid method for mutation detection and, unlike previously suggested, homozygosity for a mutation associated with dominant ankyrin-linked HS may be compatible with life.

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Gu, Wen Tao, Shao Kun Lei, Fang Li, and Shao Wei Zhou. "Research of Magnetic Grid Rail Splicing Technology Based on Phase Difference Detection Method." Applied Mechanics and Materials 278-280 (January 2013): 905–14. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.905.

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To meet the high real-time performance and high accuracy requirements of signal detection in the rail splicing process, this paper proposes a new type of magnetic grid rail splicing method based on accurately zeroing output signal phase difference of two magnetic grid reading heads, thus establishing two reading heads “shift” rules. The accurate zero setting technology of phase difference is based on Nuttall window algorithm, which doesn’t need to give the exact signal frequency beforehand and sample periodically, and can effectively eliminate phase errors. So this algorithm is suitable for detecting signal phase difference when reading heads go over buff joints with any speed at any initial position. Additionally, simulation test and experimental verification were performed on this detection algorithm and “shift” rules. The results show that, the method mentioned in this paper can real-time detect the phase difference by “shift” rules, when reading heads go over buff joints with any speed or any acceleration.

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Zhao, Dan, and Xuedong Tian. "A Multiscale Fusion Lightweight Image-Splicing Tamper-Detection Model." Electronics 11, no. 16 (August 21, 2022): 2621. http://dx.doi.org/10.3390/electronics11162621.

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The easy availability and usability of photo-editing tools have increased the number of forgery attacks, primarily splicing attacks, thereby increasing cybercrimes. Because of an existing image-splicing tamper-detection algorithm based on deep learning with high model complexity and weak robustness, a multiscale fusion lightweight model for image-splicing tamper detection is proposed. For the above problems and to improve MobileNetV2, the structural block of the classification part of the original network structure was removed, the stride of the sixth largest structural block of the network was changed to 1, the dilated convolution was used instead of downsampling, and the features extracted from the second and third large structural blocks in the network were downsampled with maximal pooling; then, the constraint on the backbone network was increased by jumping connections. Combined with the pyramid pooling module, the acquired feature layers were divided into regions of different sizes for average pooling; then, all feature layers were fused. The experimental results show that it had a low number of parameters and required a small amount of computation, achieving 91.0% and 96.4% precision on CASIA and COLUMB, respectively, and 83.2% and 88.1% F-measure on CASIA and COLUMB, respectively.

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Journal articles on the topic 'Splicing detection'

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