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Perceptual image hash has been widely investigated in an attempt to solve the problems of image content authentication and content-based image retrieval. This paper presents a perceptual image hashing algorithm which can distinguish maliciously attacked images from content-preserving ones. 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. A secure hashing scheme is developed for image authentication and finding the forgery regions. The hash can be used to find similar, forged, and different images. It can also identify the type of forgery and locate fake regions containing salient contents. The hash is very sensitive to malicious tampering. The hash of a test image is compared with the reference image. 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. Moreover, compared with some other image hashing algorithms, the proposed approach also achieves better performance even in the aspect of robustness, which is more important in some image hashing …