Multimedia Tools and Applications

, Volume 77, Issue 23, pp 30815–30840 | Cite as

Robust perceptual image hashing using fuzzy color histogram

  • Nilesh Dilipkumar GhardeEmail author
  • Dalton Meitei Thounaojam
  • Badal Soni
  • Saroj Kr. Biswas


Perceptual image hashing technique uses the appearance of the digital media object as human eye and generates a fixed size hash value. This hash value works as digital signature for the media object and it is robust against various digital manipulation done on the media object. This technique have been constantly in use in various application areas like content-based image retrieval, image authentication, digital watermarking, image copy detection, tamper detection, image indexing, etc., but it is difficult to generate a perfect perceptual image hash function due to the inverse relationship between its main properties i.e. perceptual robustness and discriminative capability. In this paper, a robust and desirable discrimination capable dual perceptual image hash functions are proposed which use fuzzy color histogram for hash generation. The fuzzy engine needs stable color representation to generate a robust fuzzy color histogram feature which is invariant to various content preserving attacks like gaussian low pass filtering, jpeg compression, etc. To satisfy this, \(CIEL^{*}a^{*}b^{*}\) color space forms an good basis as it approximates the human visual system and it is also uniform and device independent color space. The robustness of the fuzzy color histogram is further increased by selecting the most significant bins using an experimentally selected tuning factor and the same is furthermore normalized to make it scale invariant. Our experimentation shows that hash generated with this feature is more stable and able to handle various content preserving attacks and performs better as compared to the latest techniques. Both the proposed systems able to maintain good balance between perceptual robustness with optimal TPR when the FPR \(\simeq \)0 is 0.8115 and 0.8264 and discrimination capability with the optimal FPR when TPR\(\simeq \)1 is 0.0618 and 0.0208 respectively.


Image indexing Fuzzy logic Color histogram Unbiased histogram 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science & EngineeringNational Institute of Technology SilcharAssamIndia

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