Vivek, BS and Babu, RV (2020) Single-step adversarial training with dropout scheduling. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 14 - 19 June 2020, Virtual, Online, pp. 947-956.
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Abstract
Deep learning models have shown impressive performance across a spectrum of computer vision applications including medical diagnosis and autonomous driving. One of the major concerns that these models face is their susceptibility to adversarial attacks. Realizing the importance of this issue, more researchers are working towards developing robust models that are less affected by adversarial attacks. Adversarial training method shows promising results in this direction. In adversarial training regime, models are trained with mini-batches augmented with adversarial samples. Fast and simple methods (e.g., single-step gradient ascent) are used for generating adversarial samples, in order to reduce computational complexity. It is shown that models trained using single-step adversarial training method (adversarial samples are generated using non-iterative method) are pseudo robust. Further, this pseudo robustness of models is attributed to the gradient masking effect. However, existing works fail to explain when and why gradient masking effect occurs during single-step adversarial training. In this work, (i) we show that models trained using single-step adversarial training method learn to prevent the generation of single-step adversaries, and this is due to over-fitting of the model during the initial stages of training, and (ii) to mitigate this effect, we propose a single-step adversarial training method with dropout scheduling. Unlike models trained using existing single-step adversarial training methods, models trained using the proposed single-step adversarial training method are robust against both single-step and multi-step adversarial attacks, and the performance is on par with models trained using computationally expensive multi-step adversarial training methods, in white-box and black-box settings.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Computer vision; Deep learning; Diagnosis; Scheduling, Autonomous driving; Computer vision applications; Gradient ascent; Learning models; Non-iterative method; Robustness of model; SIMPLE method; Training methods, Iterative methods |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 24 Jan 2023 04:30 |
| Last Modified: | 24 Jan 2023 04:30 |
| URI: | https://eprints.iisc.ac.in/id/eprint/79295 |
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