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Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Salienc 时间:2018-11-21 14:18 来源:未知作者:admin 点击: 次 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.
1. Introduction
With the widespread use of low cost and even free editing software, people can easily 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. Generally, there are two main problems in image forensics: one is tamper detection and the other one is tamper localization. Recently, 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.
For image hashing generation, the state-of-art hashing methods could be mainly divided into two categories: data independent hashing and data dependent hashing. In conventional image hashing methods, image hash generation is a robust feature compression process without any learning stage. It includes (1) invariant feature transform based methods, such as Wavelet transform [1], Radon transform [2], Fourier-Mellin transform [3], DCT transform [4], and QFT transform [5], which aim to extract robust features from transform domains; (2) local feature points based methods, such as SIFT [6] and end-stopped wavelet [7], which take advantages of the invariant local feature under some content preserving image processing attacks; (3) dimension reduction based methods, such as singular value decomposition (SVD) [8], nonnegative matrix factorization (NMF) [9], and Fast Johnson-Lindenstrauss transform (FJLT) [10], which embed the low level features of the high dimensional space into lower dimension; (4) statistics features based methods, such as the robust image hashing with ring partition and invariant vector distance [11]. Moreover, Wang et al. [12] propose a perceptual image hashing method by combining image block based features and key-point-based features. Yan et al. [13] use a multiscale image hashing method based on the adaptive local feature.