Perceptual image hashing using transform domain noise resistant local binary pattern

Abstract

A new Discrete Cosine Transform (DCT) domain Perceptual Image Hashing (PIH) scheme is proposed in this paper. PIH schemes are designed to extract a set of features from an image to form a compact representation that can be used for image integrity verification. A PIH scheme takes an image as the input, extracts its invariant features and constructs a fixed length output, which is called a hash value. The hash value generated by a PIH scheme is then used for image integrity verification. The basic requirement for any PIH scheme is its robustness to non-malicious distortions and discriminative ability to detect minute level of tampering. The feature extraction phase plays a major role in guaranteeing robustness and tamper detection ability of a PIH scheme. The proposed scheme fuses together the DCT and Noise Resistant Local Binary Pattern (NRLBP) to compute image hash. In this scheme, an input image is divided into non-overlapping blocks. Then, DCT of each non-overlapping block is computed to form a DCT based transformed image block. Subsequently, NRLBP is applied to calculate NRLBP histogram. Histograms of all the blocks are concatenated together to get a hash vector for a single image. It is observed that low frequency DCT coefficients inherently have quite high robustness against non-malicious distortions, hence the NRLBP features extracted from the low frequency DCT coefficients provide high robustness. Computational results exhibit that the proposed hashing scheme outperforms some of the existing hashing schemes as well as can detect localized tamper detection as small as 3% of the original image size and at the same time resilient against non-malicious distortions.