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Image perceptual hashing for content authentication based on Watson’s visual model and LLE

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Abstract

Image perceptual hashing has been widely used in image content authentication. In order to extract hashing sequences that are more consistent with the subjective feeling of human’s eyes, better express the nonlinear relationship and internal structure of the original image, and more effectively distinguish the copy image from similar images, an image hashing algorithm is proposed based on Watson’s Visual Model and LLE for the content-based image authentication. First, images are prepossessed to decrease the effects of noise, interpolation and different image sizes. Then weights of DCT co-efficients of non-overlapping image blocks are adjusted by Watson’s Visual Model, and the Hu invariant moment of each image block is combined to generate an intermediate feature matrix, which is scrambled by chaotic encryption. Third, LLE is performed on the intermediate feature matrix to generate a compact hash. Finally, the compact hash is encrypted again with random numbers and quantified into 0 and 1 sequences to attain the final hash. Experimental results illustrate that when the threshold T = 0.20, the true positive rate for similar images stands at 0.9994, while the false positive rate of different images is merely 0.0017, with the total error rate reaching the least value (0.0023). Furthermore, the AUC value of the proposed algorithm is 0.999995, which is higher than that of the comparison algorithms, indicating that the algorithm has better performance than other state-of-the-art algorithms in terms of various content-preserving attacks.

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Acknowledgements

This work was supported by the Anhui Provincial Key Research and Development Plan under Grant 201904a05020091, the Provincial Natural Science Research Program of Higher Education Institutions of Anhui province under Grant KJ2021A1030 and the Key Scientific Research Projects of Chaohu University under Grant XLZ-202108.

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Chaohu University, Hefei, 238024, China

    Huifen Xing, Qilin Wu & Honghai Wang

  2. Public Service Center, Huzhou, 230018, China

    Hui Che

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  1. Huifen Xing
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  2. Hui Che
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Correspondence to Qilin Wu.

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Xing, H., Che, H., Wu, Q. et al. Image perceptual hashing for content authentication based on Watson’s visual model and LLE. J Real-Time Image Proc 20, 7 (2023). https://doi.org/10.1007/s11554-023-01269-9

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  • DOI: https://doi.org/10.1007/s11554-023-01269-9

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