A Novel Collusion Attack Strategy for Digital Fingerprinting

被引:0
作者
Ling, Hefei [1 ]
Feng, Hui [1 ]
Zou, Fuhao [1 ]
Yan, Weiqi [2 ]
Lu, Zhengding [1 ]
机构
[1] Huazhong Univ Sci Technol, Coll Comp Sci, Wuhan, Hubei, Peoples R China
[2] Queens Univ Belfast, Inst ECIT, Belfast BT7 1NN, Antrim, North Ireland
来源
DIGITAL WATERMARKING | 2011年 / 6526卷
关键词
Digital fingerprinting; collusion attack; optimization; Gaussian noise; WATERMARKING;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Digital fingerprinting is a technology which aims to embed unique marks with traceability in order to identify users who use their multimedia content for unintended purpose. A cost-efficient attack against digital fingerprinting, known as collusion attack, involves a group of users who combine their fingerprinted content for the purpose of attenuating or removing the fingerprints. In this paper, we analyze and simulate the effect of Gaussian noise with different energies added in the noise-free forgery on both the detection performance of correlation-based detector and the perceptual quality of the attacked content. Based upon the analysis and the principal of informed watermark embedding, we propose a novel collusion attack strategy, self-adaptive noise optimization (SANO) collusion attack. The experimental results, under the assumption that orthogonal fingerprints are used, show that the proposed collusion attack performs more effectively than the most of existed collusion attacks. Less than three pieces of fingerprinted content can sufficiently interrupt orthogonal fingerprints which accommodate many thousands of users. Meanwhile, high fidelity of the attacked content is retained after the proposed collusion attack.
引用
收藏
页码:224 / +
页数:3
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