Open AccessPerceptual image hash function via associative memory‐based self‐correcting
Author(s) -
Li Yuenan,
Wang Dongdong,
Wang Jingru
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.4189
Subject(s) - hash function , computer science , perception , robustness (evolution) , content addressable memory , associative property , theoretical computer science , cryptographic hash function , artificial intelligence , algorithm , mathematics , computer security , artificial neural network , pure mathematics , neuroscience , biology , biochemistry , chemistry , gene
Paralleling with the revolutionary development of the Internet, there has been increasing concern about the copyright infringement of digital media. A central problem in copyright protection is to accurately and efficiently identify the illegal copies of copyrighted contents. Perceptual hash function, which summarises the perceptual characteristics of digital media to a short digest, is a low‐cost solution to this problem. Owing to the easy‐to‐manipulate nature of digital media, a major challenge in designing perceptual hash function is to achieve the robustness against distortion. To tackle this problem, an associative memory‐based hash function is introduced. The proposed work repairs the distortions on local image structures via associative memory‐based de‐noising, in the hope of simulating the self‐correcting mechanism in human memory. The shape invariant descriptors of de‐noised structures are then encoded to binary bits. Experimental results confirm that the proposed work outperforms representative algorithms, and it can achieve an equal error rate of 3.30 × 10 − 3in content identification with only 80 bits.