# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
###### Abstract  
Robustness and discriminability are the two most important features of perceptual image hashing (PIH) schemes. In order to achieve a good balance between perceptual robustness and discriminability, a novel PIH algorithm is proposed by combining latent low-rank representation (LLRR) and rotation invariant uniform local binary patterns (RuiLBP). LLRR is first applied on resized original images to the principal feature matrix and to the salient feature matrix, since it can automatically extract salient features from corrupted images. Following this, Riulocal bin features are extracted from each non-overlapping block of the principal feature matrix and of the salient feature matrix, respectively. All features are concatenated and scrambled to generate final binary hash code. Experimental results show that the proposed hashing algorithm is robust against many types of distortions and attacks, such as noise addition, low-pass filtering, rotation, scaling, and JPEG compression. It outperforms other local binary patterns (LBP) based image hashing schemes in terms of perceptual robustness and discriminability. Keywords: perceptual image hashing; latent low-rank representation; local binary pattern; robustness +
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To achieve good robustness of image hashes, LBP has been exploited to extract suitable features in PIH schemes in recent years, and many LBP based PIH schemes have been reported in the literature. [PERSON] and [PERSON] [22] proposed a biometric hashing method based on LBP, and the biometric hash code that is generated from the LBP based histogram sequence is robust to lighting changes; however, the robustness against other content preserving operations has not been demonstrated. [PERSON] et al. [23] employed a center-symmetric local binary pattern (CSLBP) to extract image features from non-overlapping image blocks and to obtain hash values. This PIH scheme can distinguish non-malicious manipulations from malicious distortions, but it has a weak balance between robustness and discriminability. To increase the robustness, they improved the scheme by combining the singular value decomposition (SVD) before the feature extraction [24]. However, the improved PIH algorithm is not robust against geometrical distortions. [PERSON] et al. [25] used block truncation coding and CSLBP to produce an image hash, but it does not have a high robustness against noise addition. [PERSON] et al. [26] first applied SVD to create a secondary image, and then employed block truncation coding (BTC) and CSLBP to generate a compact binary hash. The results showed a satisfactory robustness to common content preserving manipulations, as well as good uniqueness, but there is no good robustness against large geometrical distortions. [PERSON] and [PERSON] [27; 28; 29; 30] designed some new PIH schemes by using improved CSLBP methods. First, original images are divided into sub-blocks in these algorithms, and some modified CSLBP approaches are used to extract 8-bin histograms as image features. Finally, double bit quantization is employed to generate a hash code for original images. Experimental results proved that the proposed schemes are robust against content preserving manipulation and that they are sensitive to content changing and structural tampering. But these PIH algorithms have low robustness against serious geometrical distortion. Considering the good performance of Noise Resistant LBP (NRLBP) in a noisy environment, [PERSON] et al. [31] presented a PIH scheme based on SVD and NRLBP, which uses SVD transformation and NRLBP to obtain suitable features for the generation of perceptual image hash values. It has enhanced the robustness to content preserving operations but does not obtain a good tradeoff between robustness and discriminability. The above mentioned PIH schemes based on LBP directly extract features from the original image and do not produce a robust secondary image with primary features. It is hard for them to achieve a good balance between robustness and discriminability.