This paper presents a feasible system for multimedia fingerprinting. One of the important problems of fingerprinting is watermarking strategies for the fingerprints, in other words, where to embed and how to embed. We address these two major problems of fingerprint-watermarking for multimedia in the proposed content-adaptive watermarking scheme. First, the fingerprint codes are often very long if the applicable resisting ability to colluders and large customer bases are needed. As a result, the acute degradation of fingerprinted content may be unacceptable. We design a strength-decision algorithm, on the basis of neural networks, to adaptively embed the long-length fingerprints with suitable magnitudes to different image regions. This adaptive watermarking technique maintains the equilibrium of the robustness and the imperceptibility without the effort to deal with visual models. Second, we analyze the disturbance of collusion attacks on images and propose an optimization algorithm which can select better embedding positions to resist collusion attacks and preserve acceptable transparency of the watermark according to different multimedia contents. In addition, we consider the lossy property of multimedia watermarking and use a sequential detection strategy to identify colluders, which can tolerate erasures and errors possibly induced in the watermarking process or communication channel. Experimental results show the high detection correctness of traitor tracing. It implies that our fingerprinting system, constructed by applying c-TA code to the content-adaptive watermarking scheme and a sequential detection algorithm, is effective for multimedia application. One can replace the fingerprint codes in our system with other existing codes to obtain effective fingerprinting systems with higher tracing correctness and practical parameters (reasonable collusion-resilience and applicable size of customer bases).
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