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Adaptive Image Transformations for Transfer-Based Adversarial Attack

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13665))

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Abstract

Adversarial attacks provide a good way to study the robustness of deep learning models. One category of methods in transfer-based black-box attack utilizes several image transformation operations to improve the transferability of adversarial examples, which is effective, but fails to take the specific characteristic of the input image into consideration. In this work, we propose a novel architecture, called Adaptive Image Transformation Learner (AITL), which incorporates different image transformation operations into a unified framework to further improve the transferability of adversarial examples. Unlike the fixed combinational transformations used in existing works, our elaborately designed transformation learner adaptively selects the most effective combination of image transformations specific to the input image. Extensive experiments on ImageNet demonstrate that our method significantly improves the attack success rates on both normally trained models and defense models under various settings.

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Notes

  1. 1.

    https://github.com/cleverhans-lab/cleverhans/tree/master/cleverhans_v3.1.0/examples/nips17_adversarial_competition/dataset.

  2. 2.

    https://drive.google.com/drive/folders/1CfobY6i8BfqfWPHL31FKFDipNjqWwAhS.

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Acknowledgments

This work is partially supported by National Key R &D Program of China (No. 2017YFA0700800), National Natural Science Foundation of China (Nos. 62176251 and Nos. 61976219).

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Zheng Yuan, Jie Zhang & Shiguang Shan

  2. University of Chinese Academy of Sciences, Beijing, China

    Zheng Yuan, Jie Zhang & Shiguang Shan

  3. Institute of Intelligent Computing Technology, Suzhou, CAS, Suzhou, China

    Jie Zhang

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  1. Zheng Yuan
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  2. Jie Zhang
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Correspondence to Jie Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Yuan, Z., Zhang, J., Shan, S. (2022). Adaptive Image Transformations for Transfer-Based Adversarial Attack. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-20065-6_1

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