Abstract
Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
Highlights
With the widespread use of low cost and even free editing software, people can create a tampered image
(3) An extensive set of experiments on image datasets demonstrates that the proposed method outperforms several state-of-the-art perceptual image hashing techniques
We proposed a novel Multi-View Semisupervised Hashing algorithm with Perceptual Saliency (MVSHPS)
Summary
With the widespread use of low cost and even free editing software, people can create a tampered image. Compared to forensic images, fake images could undergo kinds of manipulations, such as color changing, salient object changing, and copy-move forgery. There are two main problems in image forensics: one is tamper detection and the other one is tamper localization. More researchers pay attention to image tamper detection, which aims to discriminate whether a given image is pristine or fake. Image hashing based tamper detection approaches have been extensively studied recently for their great efficiency. It supports image content forensics by representing the semantic content in a compact signature, which should be robust against a wide range of content preserving attacks but sensitive to malicious manipulations
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