Adaptive Image Watermarking Scheme Using Fuzzy Entropy and GA-ELM Hybridization in DCT Domain for Copyright Protection

Abstract

A novel semi-blind image watermarking scheme based on fuzzy entropy and genetic algorithm (GA)--extreme learning machine (ELM) hybridization in discrete Cosine transform (DCT) domain for copyright protection is proposed in this paper. The selection of non overlapping blocks to embed the binary watermark is based on fuzzy entropy. As fuzzy entropy is able to discriminate data distribution under noise corrupted and redundant condition, feature extraction is more robust against various attacks. Each selected block is followed by 2-D DCT to transform it from spatial to frequency domain. Low frequency coefficients have good energy compactness and are robust to image processing attacks. As addition of noise corresponds to high frequency coefficients, these are not considered to embed the watermark in the proposed approach. The optimal scaling factor used to control the strength of watermark for each selected block of the image based on its noise sensitivity and tolerance limit is determined using GA optimization process. ELM is used to find an optimal regression function between the input feature vector (low frequency DCT coefficients) and corresponding target vector (in which the watermark bits are embedded) of each selected block. Then watermark embedding and extraction is performed intelligently by the regression function obtained by the trained ELM. The experimental results show that the proposed scheme is highly imperceptible and robust to geometric and non geometric attacks such as JPEG compression, filtering, noise addition, sharpening, gamma correction, scaling and cropping etc. To demonstrate the effectiveness of the proposed scheme, comparison with the state-of-art techniques clearly exhibits its applications for copyright protection.