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 (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.
Highlights
With the rapid development of multimedia information processing technology and the growing popularity of the Internet, the dissemination of digital contents such as digital images, audio and video via internet has become more and more popular
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
To obtain different perceptual image hash values for visually different images, a novel perceptual image hashing (PIH) scheme is proposed in this paper by using latent low rank representation (LLRR) and rotation invariant uniform local binary patterns (RiuLBP); latent low-rank representation (LLRR) is exploited to extract principal and salient features since it is able to effectively extract salient features from corrupted data; following this, a RiuLBP features extraction from principal and salient components is used to generate a final hash code
Summary
With the rapid development of multimedia information processing technology and the growing popularity of the Internet, the dissemination of digital contents such as digital images, audio and video via internet has become more and more popular. Davarzani 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 obtain different perceptual image hash values for visually different images, a novel PIH scheme is proposed in this paper by using latent low rank representation (LLRR) and rotation invariant uniform local binary patterns (RiuLBP); LLRR is exploited to extract principal and salient features since it is able to effectively extract salient features from corrupted data; following this, a RiuLBP features extraction from principal and salient components is used to generate a final hash code.
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