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High-Quality PRNU Anonymous Algorithm for JPEG Images

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Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

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Abstract

The utilization of Photo Response Non-Uniformity (PRNU) technology has found extensive application in the field of multimedia forensics, particularly in the authentication of the original camera source of an image. However, this technique has also given rise to significant concerns regarding privacy breaches. For instance, adversaries can exploit publicly available images to generate PRNU and subsequently impersonate the owners of the images. In response to these challenges, we propose an algorithm for achieving source device anonymity in widely used JPEG images. The method combines the discrete cosine transform (DCT) with JPEG compression to process the DCT coefficients of an image after inverse quantization. By ensuring the high quality of the processed image, this approach effectively breaks the link between an image and its source camera. Additionally, a reversible data hiding method is employed, enabling the recovery of traceability if necessary. Our algorithm offers several advantages over existing schemes. It operates within the domain of JPEG image compression, maintaining a low time complexity. Additionally, it effectively preserves the visual quality of images and eliminates the typical traceability effects associated with images.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (62272255, 62302248, 62302249); National key research and development program of China (2021YFC3340600, 2021YFC3340602); Taishan Scholar Program of Shandong (tsqn202306251); Jinan “New 20 Universities”-Project of Introducing Innovation Team (202228016); The “Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education (HZKY20220482); First Talent Research Project under Grant (2023RCKY131, 2023RCKY143), Integration Pilot Project of Science Education Industry under Grant (2023PX006, 2023PY060, 2023PX071).

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Authors and Affiliations

  1. Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Jian Li, Huanhuan Zhao, Bin Ma, Chunpeng Wang, Xiaoming Wu, Tao Zuo & Zhengzhong Zhao

  2. Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China

    Jian Li, Huanhuan Zhao, Bin Ma, Chunpeng Wang, Xiaoming Wu, Tao Zuo & Zhengzhong Zhao

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  1. Jian Li
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  2. Huanhuan Zhao
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  3. Bin Ma
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  4. Chunpeng Wang
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  5. Xiaoming Wu
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  6. Tao Zuo
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  7. Zhengzhong Zhao
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Correspondence to Bin Ma .

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Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi Li

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Li, J. et al. (2024). High-Quality PRNU Anonymous Algorithm for JPEG Images. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_2

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  • DOI: https://doi.org/10.1007/978-981-97-2585-4_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2584-7

  • Online ISBN: 978-981-97-2585-4

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