Robust Watermarking Using Compressed Sensing Framework with Application to MP3 Audio

In this paper a watermark embedding and recovery technique is proposed based on the compressed sensingframework. Both the watermark and the host signal are sparse, each in its own domain. In recovery, theL1-minimization is used to recover the watermark and the host signal almost perfectly in clean conditions.The proposed technique is tested on MP3 audio compression-decompression attack and additive noiseattack. Bit error rates are compared with standard spread spectrum embedding. The proposed technique isimplemented for both time domain and frequency domain embedding with a unified approach. The Walsh-Hadamard transform (WHT), the discrete cosine transform (DCT) and the Karhunen-Loeve transform(KLT) are compared in the host signal sparsifying process. Significant performance improvements in alltested conditions are achieved against the spread spectrum embedding. A payload as high as 172bps inadditive noise attacks, 86bps in 128kbps MP3 attacks and 11bps in 64kbps MP3 attacks are achieved atsmall bit error rates and acceptable MP3 audio signal quality.