Gray-scale image watermarking using GA-BPN hybrid network

• HVS characteristics are modeled successfully using GA-BPN architecture.
• Output of GA-BPN is used to embed watermark into grayscale images.
• Robustness study indicates embedding algorithm is robust against image operations.
• Watermark extraction from signed and attacked images is found to be well optimized.
• The computed time for embedding and extraction is of the order of few seconds.

In this paper, a novel watermarking scheme is proposed by embedding a binary watermark into gray-scale images using a hybrid GA-BPN intelligent network. HVS characteristics of the images in DCT domain are used to obtain a sequence of weighting factor from a GA-BPN. This weighting factor is used to embed and extract the watermark from the image in DWT domain. The GA-BPN is trained by 27 inference rules that includes three input HVS parameters namely luminance sensitivity, edge sensitivity computed using threshold and contrast sensitivity computed using variance. The robustness of the embedding scheme is examined by executing seven different image processing attacks. Visual quality of signed images before and after the attacks is examined by PSNR. The extracted watermarks from signed and attacked images show a high degree of similarity with the embedded content. Overall, the algorithm is robust against selected attacks and is well optimized.