Digital watermarking
Mean-field approaches to independent component analysis
Neural Computation
Proceedings of the 5th ACM workshop on Digital rights management
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This paper presents a blind watermark detection scheme for additive watermark embedding model. The proposed estimation-correlation-based watermark detector first estimates the embedded watermark by exploiting non-Gaussian of the real-world audio signal and the mutual independence between the host-signal and the embedded watermark and then a correlation-based detector is used to determine the presence or the absence of the watermark. For watermark estimation, blind source separation (BSS) based on underdetermined independent component analysis (UICA) is used. Low watermark-to-signal ratio (WSR) is one to the limitations of blind detection for additive embedding model. The proposed detector uses two-stage processing to improve WSR at the blind detector; first stage removes the audio spectrum from the watermarked audio signal using linear predictive (LP) filtering and the second stage uses resulting residue from the LP filtering stage to estimate the embedded watermark using BSS based on UICA. Simulation results show that the proposed detector performs significantly better than existing estimation-correlation-based detection schemes.