Perceptual image hashing using transform domain noise resistant local binary pattern
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Perceptual image hashing using transform domain noise resistant local binary pattern S. Qasim Abbas 1 & Fawad Ahmed 2 & Yi-Ping Phoebe Chen 1 Received: 17 December 2019 / Revised: 5 September 2020 / Accepted: 23 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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 nonmalicious 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. Keywords Discrete cosine transform . Local binary pattern . Perceptual image hashing . Robust hash . Transform domain hashing
* Yi-Ping Phoebe Chen [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Perceptual Image Hashing (PIH) has become a prominent research domain primarily due to speedy developments in image modification techniques that can easily alter digital images. The improvement in digital devices and networking schemes enables a user to create, broadcast, distribute, and store digital media including images and videos daily over social media networks very easily. Digital media can be easily replicated by means of copying and hence it is easy to illegally distribute or forge data. Conventionally, multimedia content integrity is accomplished by utilization of cryptographic hashing schemes. Cryptographic hashing schemes, for example, SHA-1, SHA-256 and MD5 translate original input media, for example, an image, into a fixed size binary string. Cry
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