Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

Applied Sciences

Hengfu Yang¹  Mingfang Jiang¹  Jianping Yin² 

[1] Department of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China;School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China;

关键词: perceptual image hashing;    latent low-rank representation;    local binary pattern;    robustness;   

DOI  :  10.3390/app8020317

来源: DOAJ

【 摘 要 】

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 (RiuLBP). 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.

【 授权许可】

Unknown