2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-5.1
Paper Title UNIVERSAL ADVERSARIAL ROBUSTNESS OF TEXTURE AND SHAPE-BIASED MODELS
Authors Kenneth Co, Luis Muñoz-González, Imperial College London, United Kingdom; Leslie Kanthan, DataSpartan, United Kingdom; Ben Glocker, Emil Lupu, Imperial College London, United Kingdom
SessionMLR-APPL-IP-5: Machine learning for image processing 5
LocationArea E
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
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Abstract Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture and shape-biased models to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN models with varying degrees of shape-based training. We find that shape-biased models do not markedly improve adversarial robustness, and we show that ensembles of texture and shape-biased models can improve universal adversarial robustness while maintaining strong performance.