Robust watermarking against print and scan attack through efficient modeling algorithm

• A neural network as a nonlinear system models print and scan distortions properly.
• An accurate model could suggest suitable locations in DWT domain for watermarking.
• Watermarking in the combination of DWT and DCT domains will improve the robustness.
• Only downsampling attack will degrade the robustness of watermarking algorithm.

This article proposes a blind discrete wavelet transform-discrete cosine transform (DWT-DCT) composite watermarking scheme that is robust against print and scan distortions. First, two-dimensional DWT is applied to the original image to obtain the mid-frequency subbands. Then, a one-dimensional DCT is applied to the selected mid-frequency subbands to extract the final coefficients for embedding the watermark. To specify watermarking parameters, we utilize a Genetic Algorithm to achieve a predefined image quality after watermark insertion. Suitable locations for watermarking are determined by analyzing the effect of a modeling algorithm. This model simulates noise and nonlinear attacks in printers and scanners through noise estimation and system identification methods. The experimental results demonstrate that the proposed algorithm has a high robustness against print and scan attack such that its robustness is higher than related watermarking algorithms.