As an active forensic technology, perceptual image hash has important application in image content authenticity detection and integrity authentication. In this paper, we propose a hybrid-feature-based perceptual image hash method that can be used for image tampering detection and tampering localization. In the proposed method, we use the color features of image as global features, use point-based features and block-based features as local features, and combine with the structural features to generate intermediate hash code. Then we encrypt and randomize to generate the final hash code. Using this hash code, we present a coarse-to-fine grained forensics method for image tampering detection. The proposed method can realize object-level tampering localization. Abundant experimental results show that the proposed method is sensitive to content changes caused by malicious attacks, and the tampering localization precision achieves pixel level, and it is robust to a wide range of geometric distortions and content-preserving manipulations. Compared with the state-of-the-art schemes, the proposed scheme yields superior performance.

感知图像哈希作为一种主动取证技术，在图像内容真实性检测和完整性认证方面有着重要的应用。在本文中，我们提出了一种基于混合特征的感知图像哈希方法，可用于图像篡改检测和篡改定位。在所提出的方法中，我们以图像的颜色特征作为全局特征，使用基于点的特征和基于块的特征作为局部特征，并结合结构特征生成中间哈希码。然后我们加密和随机化以生成最终的哈希码。使用此哈希码，我们提出了一种用于图像篡改检测的从粗到细的取证方法。所提出的方法可以实现对象级篡改定位。大量实验结果表明，该方法对恶意攻击引起的内容变化敏感，篡改定位精度达到像素级，对大范围的几何失真和内容保留操作具有鲁棒性。与最先进的方案相比，所提出的方案产生了优越的性能。