Digital Watermarking Based on Patchwork and Radial Basis Neural Network
Jingjing Jiang, Chi‐Man Pun · 2011
This paper presents a patchwork method for digital watermarking based on Radial Basis Neural Network (RBNN). Two special subsets of the host signal features were selected to embed the watermark signal, adding a small constant value to one subset and subtracting the same from another patch. Then, choose some sample from the embedded audio signal to train a RBNN. On the extract procedure, the RBNN obtained before will be used to verify the watermark information. The method is based on wavelet domain and the watermark signals were embedded in approximation coefficients. The quality of the watermarked signal is evaluated by PSNR (Peak Signal Noise Ratio) method and Extract Ratio(ER) after various attacks. Simulation results show that patchwork method based on Neural Network is robust against various common attacks such as filtering, resample and so on.