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Conditional Adversarial Camera Model Anonymization

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

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Abstract

The model of camera that was used to capture a particular photographic image (model attribution) is typically inferred from high-frequency model-specific artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as the process of transforming both high and low spatial frequency information. We augment the objective with the loss from a pre-trained dual-stream model attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.

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Notes

  1. 1.

    https://github.com/jeroneandrews/cama.

  2. 2.

    We refer the reader to [25] for details on this denoising filter.

  3. 3.

    Recall that all anonymization methods are trained on \((x,y)\sim q_{\mathrm {data}}(x,\mathbb {N}_{6})\).

  4. 4.

    Note that \(\mathbb {N}_{12} \setminus \mathbb {N}_{6}=\{7,\dots ,12\}\).

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Acknowledgments

JTAA is supported by the Royal Academy of Engineering (RAEng) and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Fellowship Programme.

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Authors and Affiliations

  1. Department of Computer Science, University College London, London, UK

    Jerone T. A. Andrews, Yidan Zhang & Lewis D. Griffin

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  1. Jerone T. A. Andrews
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Correspondence to Jerone T. A. Andrews .

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Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Andrews, J.T.A., Zhang, Y., Griffin, L.D. (2020). Conditional Adversarial Camera Model Anonymization. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12538. Springer, Cham. https://doi.org/10.1007/978-3-030-66823-5_13

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