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Effective PRNU extraction via densely connected hierarchical network

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Abstract

The photo-response non-uniformity (PRNU) noise of imaging sensor can be used as the fingerprint for identifying individual imaging device uniquely. As the first step of PRNU fingerprint extraction, estimating the natural noise from real-world images is rather important for source camera identification based on PRNU. The performance of most existing noise estimation schemes, which adapt to the additive white Gaussian noise (AWGN), degrade sharply for natural noise. In this paper, we present a new and effective PRNU extraction algorithm based on Densely-connected Hierarchical Denoising Network (DHDN) for source camera identification. Specifically, DHDN network is trained for dealing with different noise levels with one specific set of trained parameters. In addition, dense connectivity and residual learning are introduced to repeatedly utilize the antecedent feature maps as input and effectively solve the vanishing gradient problem for network training. Furthermore, the network can fully dig up the real-world image noise, and more PRNU related components remain in the extracted noise. By evaluating on the open digital camera and smartphone image databases, i.e., Dresden camera dataset and Daxing smartphone dataset, the proposed PRNU extraction algorithm outperforms other classical algorithms.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61972405, 61772539, 62071434), the Fundamental Research Funds for the Central Universities (Grant No. CUC21GZ010). Thanks Yunfei Hao, Xinze Hao, Yuxin Mao et al. for their hard work during the process of data collection and collation.

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Authors and Affiliations

  1. People’s Public Security University of China, Beijing, 100038, China

    Yanhui Xiao, Huawei Tian, Duo Yang & Hui Li

  2. School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China

    Gang Cao

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  1. Yanhui Xiao
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  2. Huawei Tian
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  3. Gang Cao
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  4. Duo Yang
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  5. Hui Li
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Correspondence to Huawei Tian.

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Xiao, Y., Tian, H., Cao, G. et al. Effective PRNU extraction via densely connected hierarchical network. Multimed Tools Appl 81, 20443–20463 (2022). https://doi.org/10.1007/s11042-022-12507-w

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  • DOI: https://doi.org/10.1007/s11042-022-12507-w

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