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ISO Setting Estimation Based on Convolutional Neural Network and its Application in Image Forensics

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Digital Forensics and Watermarking (IWDW 2020)

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Abstract

The ISO setting, which is also known as film speed, influences the noise characteristics of output images. As a consequence, it plays an important role in noise based forensics. Whenever the ISO setting information cannot be retrieved from the image metadata, estimating the ISO setting of a probe image from its content is of forensic significance. In this work, we propose a convolutional neural network, called ISONet, for ISO setting estimation. The proposed ISONet can successfully infer the ISO setting both globally (image-level) and locally (patch-level). It not only works on uncompressed images, but also is effective on JPEG compressed images. We apply the ISONet on two typical forensic scenarios, one is the image splicing localization and the other is the Photo Response Non-Uniformity (PRNU) correlation prediction. A series of experiments show that the ISONet can yield a remarkable improvement in both forensic scenarios.

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Notes

  1. 1.

    Source code is available at https://github.com/zengh5/ISONet.

  2. 2.

    The implementation of [13] is available in https://github.com/zengh5/Exposing-splicing-sensor-noise, and the implementations of [21, 23] are available in https://github.com/MKLab-ITI/image-forensics/tree/master/matlab_toolbox.

  3. 3.

    Source code is available at https://github.com/pkorus/multiscale-prnu.

  4. 4.

    For an ISO400 image of JPEG format, the PRNU signal is very weak even in the pristine area. Thus, the threshold here is much lower than that used in camera identification.

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Acknowledgment

We would like to thank Dr. M. Goljan for helping us in revising this manuscript, to thank the authors of [34] for their open source codes, and to thank the authors of [33] for sharing their high ISO image dataset. We would also like to thank the anonymous reviewers for their helpful suggestions. This work was supported by NSFC (grant no. 61702429), China Scholarship Council (no. 201908515095), and the Research Fund for the Doctoral Program of Southwest University of Science and Technology University (grant no. 18zx7163).

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Authors and Affiliations

  1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China

    Hui Zeng, Kang Deng & Anjie Peng

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  1. Hui Zeng
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  2. Kang Deng
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  3. Anjie Peng
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Correspondence to Anjie Peng .

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Editors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA

    Yun-Qing Shi

  3. Department of Information Engineering, University of Florence, Florence, Italy

    Alessandro Piva

  4. School of Cybersecurity, Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

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Zeng, H., Deng, K., Peng, A. (2021). ISO Setting Estimation Based on Convolutional Neural Network and its Application in Image Forensics. In: Zhao, X., Shi, YQ., Piva, A., Kim, H.J. (eds) Digital Forensics and Watermarking. IWDW 2020. Lecture Notes in Computer Science(), vol 12617. Springer, Cham. https://doi.org/10.1007/978-3-030-69449-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-69449-4_17

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