Robust Image Hashing Based on Hybrid Approach of Scale-Invariant Feature Transform and Local Binary Patterns

Jiang, Aimin; Cao, Yuan; Gao, Yuan; Tan, Rongjun; He, Haibo; Zhou, Miao

doi:10.1109/icdsp.2018.8631569

Cited by 6 publications

(1 citation statement)

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“…The reference hash will be compared with image hashes in the test database for content authentication based on the selected distance metric, such as Hamming distance. Currently the majority of perceptual hashing algorithms for authentication application can roughly be divided into the five categories: invariant feature transform-based methods [8][9][10][11][12][13], local feature points-based schemes [14][15][16][17][18][19][20][21][22][23], dimension reduction-based hashing [24][25][26][27][28][29], statistical features-based hashing [30][31][32][33][34][35] and leaning-based hashing [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

An Image Hashing Algorithm for Authentication with Multi-Attack Reference Generation and Adaptive Thresholding

Wang

et al. 2020

Algorithms

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Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

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