Thinh Nguyen, Puneet Mehra, A. Zakhor, Susie Wee, John Apostolopoulos, Wai-tian Tan, Sumit Roy, Jacob Chakareski, Eric Setton, Yi Liang, Bernd Girod, Marco Fumagalli, Cefriel-Politecnico Di Milano, Italy, P. Sagetong, Antonio Ortega, Amy R. Reibman, Vinay Vaishampayan, Rémi Ronfard, Tien Tran Thuong, France Inria, Xiaofei He, Adam Berenzweig, Daniel P W Ellis, Tong Zhang, Matthew Boutell, Yeow Kee Tan, N. Sherkat, Tony Allen, Y. Sawahata, Kiyoharu Aizawa, Timothy T H Chen, Sidney Fels, Sarah Saehee, Min, Xin Fan, China, Xing Xie, Wei-Ying Ma, Hong-Jiang Zhang, Björn Schuller, M. Zobl, G. Rigoll, Manfred Lang, Hsuan-Huei Shih, Shrikanth S Narayanan, C.-C. Jay Kuo, Rongshan Yu, Xiao Lin, S. Rahardja, Simon Lucey, Tsuhan Chen, M. Reyes-Gomez, Chih-Kai Yang, Sau-Gee Chen, Yongmin Li, Li-Qun Xu, Geoff Morrison, Charles Nightingale, J. Morphett, Jun-Wei Hsieh, John Zhang, Jagath Chen, Samarabandu, S. H. Srinivasan, M. Kankanhalli, Wei-Qi Yan, Hasan Ates, Andy Chang, Oscar C. Au, Ming Yeung, Hong Kong, Gulcin Ca
{"title":"独立多媒体指纹合谋攻击下的检测统计性能","authors":"Thinh Nguyen, Puneet Mehra, A. Zakhor, Susie Wee, John Apostolopoulos, Wai-tian Tan, Sumit Roy, Jacob Chakareski, Eric Setton, Yi Liang, Bernd Girod, Marco Fumagalli, Cefriel-Politecnico Di Milano, Italy, P. Sagetong, Antonio Ortega, Amy R. Reibman, Vinay Vaishampayan, Rémi Ronfard, Tien Tran Thuong, France Inria, Xiaofei He, Adam Berenzweig, Daniel P W Ellis, Tong Zhang, Matthew Boutell, Yeow Kee Tan, N. Sherkat, Tony Allen, Y. Sawahata, Kiyoharu Aizawa, Timothy T H Chen, Sidney Fels, Sarah Saehee, Min, Xin Fan, China, Xing Xie, Wei-Ying Ma, Hong-Jiang Zhang, Björn Schuller, M. Zobl, G. Rigoll, Manfred Lang, Hsuan-Huei Shih, Shrikanth S Narayanan, C.-C. Jay Kuo, Rongshan Yu, Xiao Lin, S. Rahardja, Simon Lucey, Tsuhan Chen, M. Reyes-Gomez, Chih-Kai Yang, Sau-Gee Chen, Yongmin Li, Li-Qun Xu, Geoff Morrison, Charles Nightingale, J. Morphett, Jun-Wei Hsieh, John Zhang, Jagath Chen, Samarabandu, S. H. Srinivasan, M. Kankanhalli, Wei-Qi Yan, Hasan Ates, Andy Chang, Oscar C. Au, Ming Yeung, Hong Kong, Gulcin Ca","doi":"10.1109/ICME.2003.1220890","DOIUrl":null,"url":null,"abstract":"Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we consider average attack and several nonlinear collusion attacks on independent Gaussian based fingerprints, and study the detection performance of several commonly used detection statistics in the literature under collusion attacks. Observing that these detection statistics are not specifically designed for collusion scenarios and do not take into account the characteristics of the newly generated fingerprints under collusion attacks, we propose pre-processing techniques to improve the detection performance of the detection statistics under collusion attacks.","PeriodicalId":118560,"journal":{"name":"2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Performance of detection statistics under collusion attacks on independent multimedia fingerprints\",\"authors\":\"Thinh Nguyen, Puneet Mehra, A. 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Performance of detection statistics under collusion attacks on independent multimedia fingerprints
Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we consider average attack and several nonlinear collusion attacks on independent Gaussian based fingerprints, and study the detection performance of several commonly used detection statistics in the literature under collusion attacks. Observing that these detection statistics are not specifically designed for collusion scenarios and do not take into account the characteristics of the newly generated fingerprints under collusion attacks, we propose pre-processing techniques to improve the detection performance of the detection statistics under collusion attacks.
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