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Recaptured image forensics based on normalized local ternary count histograms of residual maps

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Abstract

Image display and acquisition technologies have greatly developed over the past few years, which make it possible to recapture high-quality images from high-fidelity LCD (liquid crystal display) screens. These recaptured images pose serious threats on current image forensics technologies and various intelligent recognition systems, which makes the detection of such images significantly important. In this paper, we propose a recaptured image detection method based on normalized LTC (local ternary count) histograms of residual maps. Specifically, given an image, the residual maps of itself and its downsampled version are calculated in gray space via pixel-wise adaptive Wiener filtering. Then, the normalized LTC histograms of all the residual maps are extracted and concatenated as the final feature for training and testing. Single database experiments demonstrate that our proposed method not only performs very close to the state-of-the-art methods on relative low-quality NTU-ROSE and BJTU-IIS databases, but also improves the performance on the most difficult-to-detect ICL-COMMSP database obviously, which verifies the effectiveness of the proposed algorithm. Besides, mixed database experiments verify the superiority on generalization ability of our proposed method. Moreover, it has a lower computational complexity.

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Notes

  1. The code is available at https://github.com/nanzhu-DMFLab/recaptured-image-detection-SIVP-2021

  2. http://rose1.ntu.edu.sg/Datasets/recapturedImages.asp

  3. http://www.commsp.ee.ic.ac.uk/~pld/research/Rewind/Recapture/

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Acknowledgements

The authors would like to thank Dr. Wang for making his source code of [22] freely available online. We would also want to thank Dr. Sun for sharing us the source code of [16] for comparison. Besides, we also want to thank Prof. Ni for sharing the BJTU-IIS database for experiments. This work was supported by the National Natural Science Foundation of China (No. 61901349), the Natural Science Basic Research Program of Shaanxi (No. 2019JQ-322).

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Authors and Affiliations

  1. Department of Electronic Information Engineering, Xi’an Technological University, Xi’an, 710021, China

    Nan Zhu, Quanmin Guo, Man Cui & Xiaolu Guo

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  1. Nan Zhu
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  2. Quanmin Guo
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  3. Man Cui
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  4. Xiaolu Guo
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Correspondence to Nan Zhu.

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Zhu, N., Guo, Q., Cui, M. et al. Recaptured image forensics based on normalized local ternary count histograms of residual maps. SIViP 16, 165–173 (2022). https://doi.org/10.1007/s11760-021-01974-7

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  • DOI: https://doi.org/10.1007/s11760-021-01974-7

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