An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

Abstract

A novel adaptive semi-fragile watermarking scheme for tamper detection and recovery of digital images is proposed in this paper. This scheme involves embedding of content and chroma watermarks generated from the first level Discrete Curvelet Transform (DCLT) coarse coefficients. Embedding is performed by quantizing the first level coarse DCLT coefficients of the input image and amount of quantization is intelligently decided based on the energy contribution of the coefficients. During watermark extraction, a tampered matrix is generated by comparing the feature similarity index value between each block of extracted and generated watermarks. The tampered objects are subsequently identified and an intelligent report is formed based on their severity classes. The recovery of the tampered objects is performed using the generated DCLT coefficients from luminance and chrominance components of the watermarked image. Results reveal that the proposed method outperforms existing method in terms of tamper detection and recovery of digital images.