A Visual Model-Based Perceptual Image Hash for Content Authentication

Perceptual image hash has been widely investigated in an attempt to solve the problems of image content authentication and content-based image retrieval. In this paper, we combine statistical analysis methods and visual perception theory to develop a real perceptual image hash method for content authentication. To achieve real perceptual robustness and perceptual sensitivity, the proposed method uses Watson´s visual model to extract visually sensitive features that play an important role in the process of humans perceiving image content. We then generate robust perceptual hash code by combining image-block-based features and key-point-based features. The proposed method achieves a tradeoff between perceptual robustness to tolerate content-preserving manipulations and a wide range of geometric distortions and perceptual sensitivity to detect malicious tampering. Furthermore, it has the functionality to detect compromised image regions. Compared with state-of-the-art schemes, the proposed method obtains a better comprehensive performance in content-based image tampering detection and localization.