Academic literature on the topic 'Splicing detection'
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Journal articles on the topic "Splicing detection"
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.
Full textŠetrajčič 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.
Full textYan, 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.
Full textRamirez-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.
Full textSrinivasan, 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.
Full textAlshwely, 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.
Full textKadam, 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.
Full textSu, 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.
Full textAnna, 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.
Full textLi, 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.
Full textDissertations / Theses on the topic "Splicing detection"
Sugnet, Charles W. "Discovery and detection of alternative splicing /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Full textDevagiri, Vishnu Manasa. "Splicing Forgery Detection and the Impact of Image Resolution." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14060.
Full textJulliand, Thibault. "Automatic noise-based detection of splicing in digital images." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC2057.
Full textIn this dissertation, we offer three new forensics imagery methods to detect splicing in digital images by exploiting image noise statistics. To do so, we introduce a new tool, the noise density histogram, and its derivative, the noise density contribution histogram. Our methods allow splicing detection on both raw and JPEG images. Although the use of noise discrepancies to detect splicing has already been done multiple times, most existing methods tend to perform poorly on the current generation of high quality images, with high resolution and low noise. The effectiveness of our approaches are demonstrated over a large set of such images, with randomly-generated splicings. We also present a detailed analysis of the evolution of the noise in a digital camera, and how it affects various existing forensics approaches. In a final part, we use the tool we developed in a counter-forensics approach, in order to hide the trace left by splicing on the image noise
Chern, Tzu-Ming. "Low detection of exon skipping in mouse genes orthologous to human genes on chromosome 22." Thesis, University of the Western Cape, 2002. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6894_1185440491.
Full textAlternative RNA splicing is one of the leading mechanisms contributing towards transcript and protein diversity. Several alternative splicing surveys have confirmed the frequent occurrence of exon skipping in human genes. However, the occurrence of exon skipping in mouse genes has not yet been extensively examined. Recent improvements in mouse genome sequencing have permitted the current study to explore the occurrence of exon skipping in mouse genes orthologous to human genes on chromosome 22. A low number (5/72 multi-exon genes) of mouse exon-skipped genes were captured through alignments of mouse ESTs to mouse genomic contigs. Exon-skipping events in two mouse exon-skipped genes (GNB1L, SMARCB1) appear to affect biological processes such as electron and protein transport. All mouse, skipped exons were observed to have ubiquitous tissue expression. Comparison of our mouse exon-skipping events to previously detected human exon-skipping events on chromosome 22 by Hide et al.2001, has revealed that mouse and human exon-skipping events were never observed together within an orthologous gene-pair. Although the transcript identity between mouse and human orthologous transcripts were high (greater than 80% sequence identity), the exon order in these gene-pairs may be different between mouse and human orthologous genes.
Main factors contributing towards the low detection of mouse exon-skipping events include the lack of mouse transcripts matching to mouse genomic sequences and the under-prediction of mouse exons. These factors resulted in a large number (112/269) of mouse transcripts lacking matches to mouse genomic contigs and nearly half (12/25) of the mouse multi-exon genes, which have matching Ensembl transcript identifiers, have under-predicted exons. The low frequency of mouse exon skipping on chromosome 22 cannot be extrapolated to represent a genome-wide estimate due to the small number of observed mouse exon-skipping events. However, when compared to a higher estimate (52/347) of exon skipping in human genes for chromosome 22 produced under similar conditions by Hide et al.2001, it is possible that our mouse exon-skipping frequency may be lower than the human frequency. Our hypothesis contradicts with a previous study by Brett et al.2002, in which the authors claim that mouse and human alternative splicing is comparable. Our conclusion that the mouse exon-skipping frequency may be lower than the human estimate remains to be tested with a larger mouse multi-exon gene set. However, the mouse exon-skipping frequency may represent the highest estimate that can be obtained given that the current number (87) of mouse genes orthologous to chromosome 22 in Ensembl (v1 30th Jan. 2002) does not deviate significantly from our total number (72) of mouse multi-exon genes. The quality of the current mouse genomic data is higher than the one utilized in this study. The capture of mouse exon-skipping events may increase as the quality and quantity of mouse genomic and transcript sequences improves.
Venkatasubramanian, Meenakshi. "De novo Population Discovery from Complex Biological Datasets." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563873297599047.
Full textRomero, barrios Natali. "Non-codings RNAs, regulators of gene expression in Arabidopsis thaliana root developmental plasticity Noncoding Transcription by Alternative RNA Polymerases Dynamically Regulates an Auxin-Driven Chromatin Loop Battles and hijacks: noncoding transcription in plants Long noncoding RNA modulates alternative splicing regulators in Arabidopsis Detection of generic differential RNA processing events from RNA-seq data." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS128.
Full textIn the last years, high-throughput sequencing techniques have made possible to identify thousands of noncoding RNAs and a plethora of different mRNA processing events occurring in higher organisms. This led to a better understanding of different regulatory mechanisms controlling gene expression. Long noncoding RNAs (lncRNAs) are emerging as key players in the regulation of varied developmental processes. They can act directly in a long form by lncRNA-protein interactions or be processed into shorter small si/miRNAs, leading to mRNA cleavage, translational repression or epigenetic DNA/chromatin modification of their targets. In this study, we aim to understand the mechanism of action of lncRNAs in plant development. Initially, I contributed to the analysis of the action of the APOLO lncRNA in chromatin topology regulation. Then, I focused my work on the lncRNA ASCO (Alternative Splicing COmpetitor) that interacts with NSRs (Nuclear Speckles RNA-binding Proteins) to modulate the splicing pattern of NSR-regulated mRNA targets. Auxin treatment induces NSRb and represses ASCO expression in roots. The nsra/b double mutant and ASCO overexpressing lines treated with auxin are partially impaired in lateral root formation. Using a new bioinformatic tool called “RNAprof”, we detected 1885 differential RNA processing events genome-wide in auxin-treated nsra/b mutants compared to WT. Among them, we identified ARF19, a key regulator of auxin signaling in lateral root initiation and development. I demonstrated that ARF19 is directly bound by both NSRs and that in the nsra/b double mutant ARF19 is alternatively polyadenylated leading to a short transcript isoform. Furthermore, among the transcriptionally deregulated genes in the nsra/b mutant plants, I identified an important group related to ethylene response. I further showed that several of these genes are also deregulated in the arf19-1 and arf19-2 mutants plants in response to auxin, supporting a role of ARF19 in the auxin-ethylene crosstalk. NSRb is also induced by ethylene and the inhibition of ethylene synthesis by AVG rescues the nsra/b double mutant lateral root phenotype in response to auxin. Moreover, AVG and ASCO overexpression lead to increased accumulation of the ARF19 short isoform. Altogether, this study shed new light on the role of the lncRNA ASCO in the regulation of RNA processing by hijacking NSRs and the capacity of non-coding RNAs to modulate splicing
Abecidan, Rony. "Stratégies d'apprentissage robustes pour la détection de manipulation d'images." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0025.
Full textToday, it is easier than ever to manipulate images for unethical purposes. This practice is therefore increasingly prevalent in social networks and advertising. Malicious users can for instance generate convincing deep fakes in a few seconds to lure a naive public. Alternatively, they can also communicate secretly hidding illegal information into images. Such abilities raise significant security concerns regarding misinformation and clandestine communications. The Forensics community thus actively collaborates with Law Enforcement Agencies worldwide to detect image manipulations. The most effective methodologies for image forensics rely heavily on convolutional neural networks meticulously trained on controlled databases. These databases are actually curated by researchers to serve specific purposes, resulting in a great disparity from the real-world datasets encountered by forensic practitioners. This data shift addresses a clear challenge for practitioners, hindering the effectiveness of standardized forensics models when applied in practical situations.Through this thesis, we aim to improve the efficiency of forensics models in practical settings, designing strategies to mitigate the impact of data shift. It starts by exploring literature on out-of-distribution generalization to find existing strategies already helping practitioners to make efficient forensic detectors in practice. Two main frameworks notably hold promise: the implementation of models inherently able to learn how to generalize on images coming from a new database, or the construction of a representative training base allowing forensics models to generalize effectively on scrutinized images. Both frameworks are covered in this manuscript. When faced with many unlabeled images to examine, domain adaptation strategies matching training and testing bases in latent spaces are designed to mitigate data shifts encountered by practitioners. Unfortunately, these strategies often fail in practice despite their theoretical efficiency, because they assume that scrutinized images are balanced, an assumption unrealistic for forensic analysts, as suspects might be for instance entirely innocent. Additionally, such strategies are tested typically assuming that an appropriate training set has been chosen from the beginning, to facilitate adaptation on the new distribution. Trying to generalize on a few images is more realistic but much more difficult by essence. We precisely deal with this scenario in the second part of this thesis, gaining a deeper understanding of data shifts in digital image forensics. Exploring the influence of traditional processing operations on the statistical properties of developed images, we formulate several strategies to select or create training databases relevant for a small amount of images under scrutiny. Our final contribution is a framework leveraging statistical properties of images to build relevant training sets for any testing set in image manipulation detection. This approach improves by far the generalization of classical steganalysis detectors on practical sets encountered by forensic analyst and can be extended to other forensic contexts
PANT, MALLIKA. "DIGITAL SPLICING DETECTION USING LOCAL INVARIANT FEATURES." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14798.
Full textBarros, Pedro Miguel Rodrigues de. "Alternative splicing detection across different tissues in cork oak." Master's thesis, 2017. http://hdl.handle.net/10451/31981.
Full textAs florestas de sobreiro (Quercus suber L.) são recursos únicos e emblemáticos em Portugal, com elevado impacto económico, ecológico e social. A disponibilidade recente da sequência do genoma de sobreiro forneceu um importante contributo para revitalizar a pesquisa em temas como desenvolvimento de cortiça e melhoramento da planta, assim como promover a competitividade da indústria da cortiça. No entanto, é ainda necessário adicionar mais detalhe à anotação estrutural do genoma, nomeadamente ao nível dos transcritos, incluindo previsão de eventos de splicing alternativo. O splicing alternativo (AS) é um processo usado durante a expressão génica que origina diferentes variantes de transcritos (isoformas) e produtos proteicos a partir um único gene. No presente estudo, procedemos à análise de dezasseis bibliotecas de RNA-seq, preparadas a partir de quatro tecidos de sobreiro (folhas, felema, entrecasco e xilema), de modo a prever novas formas de AS para genes já previstos e melhorar a anotação estrutural do genoma. Um protocolo bioinformático foi definido para testar o desempenho do software HISAT2 e STAR para mapeamento de reads de RNAseq no genoma de referência, e do software Cufflinks e StringTie para (re)construção de transcritos. O alinhamento de reads no genoma efetuado com STAR resultou em taxas de mapeamento (de 84,22% a 86,86%) superiores aos resultados atingidos com HISAT2 (73,88% a 76,55%). Assim, os resultados de mapeamento com STAR foram utilizados para a (re)construção de transcritos. O uso do StringTie para este processo foi globalmente mais conservador do que com Cufflinks, gerando menos transcritos novos, mas com melhor cobertura de reads por pares de base. Para melhorar a precisão da anotação e reduzir falsos positivos, foi realizado um passo adicional de otimização com StringTie. Desta otimização resultou uma anotação que prevê a ocorrência de 7 958 novos transcritos (8% dos transcritos totais), dos quais 5 453 são novas isoformas para genes previstos na anotação de referência. Esta nova anotação foi utilizada como referência para estimar a abundância dos transcritos em cada um dos tecidos estudados e efetuar a análise de expressão diferencial. Cerca de 16% de todos os genes expressos nos quatro tecidos e que contêm intrões apresentaram splicing alternativo, e os principais eventos de splicing foram alternative acceptor site e intron retention. Grupos de transcritos com expressão diferencial entre os quatro tecidos foram identificados e a análise de enriquecimento funcional confirmou os principais processos biológicos esperados para cada tecido: os transcritos mais expressos nas folhas e no xilema estavam relacionados com a fotossíntese e com transporte, respetivamente; transcritos mais expressos na periderme (felema e entrecasco) mostraram um enriquecimento em categorias funcionais relacionadas com a síntese de suberina e outros componentes de parede celular presentes nas células de cortiça. Estes grupos específicos mostraram também um enriquecimento em transcritos envolvidos na resposta ao stresse (biótico ou abiótico). Nos tecidos que compõem a periderme, este enriquecimento foi observado principalmente no entrecasco, enquanto que no felema foi detetado um enriquecimento em transcritos envolvidos no metabolismo secundário. A presente tese permitiu a definição de um protocolo padrão que poderá ser usado para estudar o splicing alternativo no sobreiro e para uma análise mais aprofundada na nova versão do genoma, que estará disponível em breve.
Cork oak (Quercus suber L.) forests are unique and emblematic resources for Portugal, with high economical, ecological and social significance. The recent availability of the cork oak genome sequence provided an important contribution to reinvigorate research in fundamental topics such as cork development and plant improvement, and to promote the competitiveness of cork industry. Yet, further analysis is required to add detail to genome structure annotation, namely at the transcript level, also taking into account alternative splicing. Alternative splicing (AS) is a process used during gene expression to yield different transcript variants and protein products derived from a single gene. In the present study, we analyzed sixteen RNA-seq libraries prepared from four cork oak tissues (leaf, xylem, phellem and inner bark), in order to predict new AS forms for the already predicted genes and improve genome structural annotation. A bioinformatics pipeline was defined in order to test the performance of HISAT2 and STAR for read mapping against the reference genome, and Cufflinks and StringTie for transcript assembly. STAR yielded higher mapping efficiencies (84.22% to 86.86%) for the cork oak datasets, as compared to HISAT2 (73.88% to 76.55%), and the corresponding mapping data was selected for transcript assembly. The use of StringTie for this step was globally more conservative than Cufflinks, generating less novel transcripts, but with better support by read per base coverage. A further optimization step was performed using StringTie in order to improve annotation precision. The final transcript annotation was selected from this optimization step, predicting 7,958 novel transcripts (8% of total transcripts in the new annotation), 5,453 of which were novel isoforms for genes in reference annotation. This new annotation was used as reference to estimate transcript abundance in each tissue and differential expression analysis. Approximately 16% of all intron-containing genes expressed in the four tissues were alternatively spliced and the main event found in the four cork oak tissues was alternative acceptor site, followed by intron retention. Transcript clusters showing differential expression among the four tissues were identified and functional enrichment analysis confirmed the main biological processes expected for each tissue: transcripts highly expressed in leaves and xylem were mostly related to photosynthesis and transport, respectively; transcripts highly expressed in peridermis (phellem and inner bark) showed an enrichment in functional categories related to the synthesis of suberin and other component of cork cell walls. These tissue-specific clusters also showed an enrichment in transcripts involved in the response to stress (biotic or abiotic). Yet, in peridermis, this enrichment was mostly observed in inner bark samples, while phellem samples showed an enrichment in transcripts related to secondary metabolism. This thesis allowed the definition of a standard workflow that can be used to study alternative splicing in cork oak and used for further analysis on the new improved genome version that will be available soon.
卓宥亦, Yu-Yi Cho, and 卓宥亦. "Image Splicing Detection Using Inconsistent Multi-scale Local Noise Variances." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/ygjchs.
Full text國立臺北科技大學
電機工程系所
101
Due to the rapid development of image editing software, it becomes easy to tamper a digital image and create a fake image. One of the common and simple ways of the digital image tampering is image splicing, which is to copy a region of an image to another image, and add the contents not belonging to the original image. Therefore, the authenticity of digital image has become an important issue. Since different sources of images are productions of different sensors and post-processing, they will contain different noise levels. Based on this fact, we propose a multi-scale PCA noise level estimation algorithm to detect image splicing, and improve the detection rate of the existing works. In addition, our method can locate the tampering region by clustering the estimated noise with the integration of EGB segmentation. In the experiments, we use the Columbia uncompressed image splicing detection evaluation dataset to test our method. The experimental results show that our method can detect the image splicing robustly and accurately.
Book chapters on the topic "Splicing detection"
Shi, Yun Q., Chunhua Chen, Guorong Xuan, and Wei Su. "Steganalysis Versus Splicing Detection." In Digital Watermarking, 158–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92238-4_13.
Full textKim, Namshin, and Christopher Lee. "Bioinformatics Detection of Alternative Splicing." In Bioinformatics, 179–97. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_9.
Full textZhang, Zhen, Ying Zhou, Jiquan Kang, and Yuan Ren. "Study of Image Splicing Detection." In Lecture Notes in Computer Science, 1103–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87442-3_136.
Full textNagtode, Smita A., and Shrutika A. Korde. "Splicing Detection Technique for Images." In Lecture Notes on Data Engineering and Communications Technologies, 654–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24643-3_78.
Full textKuroyanagi, Hidehito, Akihide Takeuchi, Takayuki Nojima, and Masatoshi Hagiwara. "Single-Cell Detection of Splicing Events with Fluorescent Splicing Reporters." In Alternative pre-mRNA Splicing, 298–309. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527636778.ch28.
Full textShen, Manli, and Michael G. Fried. "Detection of RNA-Protein Complexes by Electrophoretic Mobility Shift Assay." In Alternative pre-mRNA Splicing, 182–98. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527636778.ch17.
Full textSapra, Aparna K., Fernando Carrillo Oesterreich, Marta Pabis, Imke Listerman, Nicole Bardehle, and Karla M. Neugebauer. "Splicing Factor ChIP and ChRIP: Detection of Splicing and Splicing Factors at Genes by Chromatin Immunoprecipitation." In Alternative pre-mRNA Splicing, 416–27. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527636778.ch39.
Full textVlachoyiannopoulos, Panayiotis G. "Detection of Autoantibodies to Extractable Cellular Antigens." In RNP Particles, Splicing and Autoimmune Diseases, 141–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80356-7_6.
Full textLiu, Jin, Hefei Ling, Fuhao Zou, and Zhengding Lu. "Image Splicing Detection Using Multi-resolution Histogram." In Advances in Multimedia Information Processing - PCM 2009, 858–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_76.
Full textZhao, Xudong, Shilin Wang, Shenghong Li, Jianhua Li, and Xiang Lin. "A Distributed Scheme for Image Splicing Detection." In Digital-Forensics and Watermarking, 314–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43886-2_23.
Full textConference papers on the topic "Splicing detection"
Kanca, Elif, Tolgahan Gulsoy, Arda Ustubioglu, Beste Ustubioglu, Selen Ayas, Elif Baykal Kablan, and Guzin Ulutas. "Deep Learning-Based Audio Splicing Forgery Detection Using PVTv2 Transformer." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5. IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10757148.
Full textMoghaddasi, Zahra, Hamid A. Jalab, and Rafidah Md Noor. "SVD-based image splicing detection." In 2014 International Conference on Information Technology and Multimedia (ICIMU). IEEE, 2014. http://dx.doi.org/10.1109/icimu.2014.7066598.
Full textMushtaq, Saba, and Ajaz Hussain Mir. "Novel method for image splicing detection." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968386.
Full textZhang, Zhen, GuangHua Wang, Yukun Bian, and Zhou Yu. "A novel model for splicing detection." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645135.
Full textMatern, Falko, Christian Riess, and Marc Stamminger. "Depth Map Fingerprinting and Splicing Detection." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9052979.
Full textUstubioglu, Beste, Samet Dincer, Arda Ustubioglu, and Guzin Ulutas. "ArCapsNet for Audio Splicing Forgery Detection." In 2024 47th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2024. http://dx.doi.org/10.1109/tsp63128.2024.10605934.
Full textJin, Wa, Weihong Bi, and Guangwei Fu. "Optimal fusion offset in splicing photonic crystal fibers." In ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, edited by Yunjiang Rao. SPIE, 2013. http://dx.doi.org/10.1117/12.2034350.
Full textKaizhen, Zhu, and Zhen Zhang. "A Novel Algorithm of Image Splicing Detection." In 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.512.
Full textFan, Yu, Philippe Carre, and Christine Fernandez-Maloigne. "Image splicing detection with local illumination estimation." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351341.
Full textZhang, Zhen, Jiquan Kang, and Yuan Ren. "An Effective Algorithm of Image Splicing Detection." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1621.
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