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A Visual Model-Based Perceptual Image Hash for Content Authentication

机译：用于内容认证的基于可视模型的感知图像哈希

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摘要

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.
机译：为了解决图像内容认证和基于内容的图像检索问题，对感知图像哈希进行了广泛的研究。在本文中，我们将统计分析方法和视觉感知理论相结合，开发出一种用于内容认证的真实感知图像哈希方法。为了获得真实的感知鲁棒性和感知敏感性，该方法使用了Watson的视觉模型来提取视觉敏感特征，这些特征在人类感知图像内容的过程中起着重要作用。然后，我们通过结合基于图像块的功能和基于关键点的功能来生成健壮的感知哈希代码。所提出的方法在容忍内容保留操作的感知鲁棒性与检测恶意篡改的广泛几何变形和感知敏感性之间达成了折衷。此外，它具有检测受损图像区域的功能。与最新方案相比，该方法在基于内容的图像篡改检测和定位中获得了更好的综合性能。

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