Letteratura scientifica selezionata sul tema "Cover source mismatch"
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Articoli di riviste sul tema "Cover source mismatch"
Giboulot, Quentin, Rémi Cogranne, Dirk Borghys e Patrick Bas. "Effects and solutions of Cover-Source Mismatch in image steganalysis". Signal Processing: Image Communication 86 (agosto 2020): 115888. http://dx.doi.org/10.1016/j.image.2020.115888.
Testo completoYu, Lifang, Shaowei Weng, Mengfei Chen e Yunchao Wei. "RCDD: Contrastive domain discrepancy with reliable steganalysis labeling for cover source mismatch". Expert Systems with Applications 237 (marzo 2024): 121543. http://dx.doi.org/10.1016/j.eswa.2023.121543.
Testo completoHu, Donghui, Zhongjin Ma, Yuqi Fan, Shuli Zheng, Dengpan Ye e Lina Wang. "Study on the interaction between the cover source mismatch and texture complexity in steganalysis". Multimedia Tools and Applications 78, n. 6 (15 agosto 2018): 7643–66. http://dx.doi.org/10.1007/s11042-018-6497-0.
Testo completoKauko, Hanne, Daniel Rohde, Brage Rugstad Knudsen e Terje Sund-Olsen. "Potential of Thermal Energy Storage for a District Heating System Utilizing Industrial Waste Heat". Energies 13, n. 15 (31 luglio 2020): 3923. http://dx.doi.org/10.3390/en13153923.
Testo completoWang, Peipei, Yun Cao e Xianfeng Zhao. "Segmentation Based Video Steganalysis to Detect Motion Vector Modification". Security and Communication Networks 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8051389.
Testo completoStreckienė, Giedrė, e Salomėja Bagdonaitė. "MODELLING THE SIZE OF SEASONAL THERMAL STORAGE IN THE SOLAR DISTRICT HEATING SYSTEM / SEZONINĖS ŠILUMOS AKUMULIACINĖS TALPYKLOS TŪRIO MODELIAVIMAS CENTRALIZUOTOJE SAULĖS ŠILDYMO SISTEMOJE". Mokslas - Lietuvos ateitis 5, n. 4 (31 dicembre 2012): 499–506. http://dx.doi.org/10.3846/mla.2012.80.
Testo completoKartini, Dede Sri, Neneng Yani Yuningsih e Iyep Saefulrahman. "Consistency between Political Party’s Program and Policy Product in Bandung Regency". MIMBAR, Jurnal Sosial dan Pembangunan 33, n. 2 (20 dicembre 2017): 267. http://dx.doi.org/10.29313/mimbar.v33i2.2330.
Testo completoWang, Mo, Furong Chen, Dongqing Zhang, Qiuyi Rao, Jianjun Li e Soon Keat Tan. "Supply–Demand Evaluation of Green Stormwater Infrastructure (GSI) Based on the Model of Coupling Coordination". International Journal of Environmental Research and Public Health 19, n. 22 (9 novembre 2022): 14742. http://dx.doi.org/10.3390/ijerph192214742.
Testo completoFRANCIS, ELAINE J., e ETSUYO YUASA. "A multi-modular approach to gradual change in grammaticalization". Journal of Linguistics 44, n. 1 (5 febbraio 2008): 45–86. http://dx.doi.org/10.1017/s0022226707004951.
Testo completoLasch-Born, Petra, Felicitas Suckow, Christopher P. O. Reyer, Martin Gutsch, Chris Kollas, Franz-Werner Badeck, Harald K. M. Bugmann et al. "Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites". Geoscientific Model Development 13, n. 11 (5 novembre 2020): 5311–43. http://dx.doi.org/10.5194/gmd-13-5311-2020.
Testo completoTesi sul tema "Cover source mismatch"
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.
Testo completoToday, 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
Capitoli di libri sul tema "Cover source mismatch"
Zhang, Xunpeng, Xiangwei Kong, Pengda Wang e Bo Wang. "Cover-Source Mismatch in Deep Spatial Steganalysis". In Digital Forensics and Watermarking, 71–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43575-2_6.
Testo completoHu, Donghui, Zhongjin Ma, Yuqi Fan e Lina Wang. "A Study of the Two-Way Effects of Cover Source Mismatch and Texture Complexity in Steganalysis". In Digital Forensics and Watermarking, 601–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53465-7_45.
Testo completoMahto, Rakeshkumar, e Reshma John. "Modeling of Photovoltaic Module". In Solar Cells [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97082.
Testo completoAtti di convegni sul tema "Cover source mismatch"
Quentin, Giboulot, Bas Patrick, Cogranne Remi e Borghys Dirk. "The Cover Source Mismatch Problem in Deep-Learning Steganalysis". In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909553.
Testo completoBorghys, Dirk, Patrick Bas e Helena Bruyninckx. "Facing the Cover-Source Mismatch on JPHide using Training-Set Design". In IH&MMSec '18: 6th ACM Workshop on Information Hiding and Multimedia Security. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206004.3206021.
Testo completoMallet, Antoine, Patrick Bas e Rémi Cogranne. "Statistical Correlation as a Forensic Feature to Mitigate the Cover-Source Mismatch". In IH&MMSEC '24: ACM Workshop on Information Hiding and Multimedia Security. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3658664.3659638.
Testo completoLi, Zhenyu, e Adrian G. Bors. "Selection of robust features for the Cover Source Mismatch problem in 3D steganalysis". In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900302.
Testo completoKodovský, Jan, Vahid Sedighi e Jessica Fridrich. "Study of cover source mismatch in steganalysis and ways to mitigate its impact". In IS&T/SPIE Electronic Imaging, a cura di Adnan M. Alattar, Nasir D. Memon e Chad D. Heitzenrater. SPIE, 2014. http://dx.doi.org/10.1117/12.2039693.
Testo completoMéreur, Arthur, Antoine Mallet e Rémi Cogranne. "Are Deepfakes a Game-changer in Digital Images Steganography Leveraging the Cover-Source-Mismatch?" In ARES 2024: The 19th International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3664476.3670893.
Testo completoBenes, Martin, Nora Hofer e Rainer Bohme. "The Effect of the JPEG Implementation on the Cover-Source Mismatch Error in Image Steganalysis". In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909711.
Testo completoOliveira, Daniel, Paolo Rech e Philippe Olivier Navaux. "Hardening Strategies for HPC Applications". In XX Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wscad_estendido.2019.8708.
Testo completoBowling, Kim. "Geometry Detector Teardown Results at CSX". In ASME 2009 Rail Transportation Division Fall Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/rtdf2009-18026.
Testo completoFeneley, Adam, Apostolos Pesiridis e Hua Chen. "A One-Dimensional Gas Dynamics Code for Turbocharger Turbine Pulsating Flow Performance Modelling". In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64743.
Testo completo