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Make some noise: reliable and efficient single-step adversarial training
at
| Pau De Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Gregory Rogez, Puneet K. Dokania |
| Neural Information Processing Systems (NeurIPS), hybrid event, New Orleans, Louisiana, USA, 28 November – 9 December, 2022 |
| arXiv |
| OpenReview.net |
@inproceedings{
jorge2022make,
title={Make Some Noise: Reliable and Efficient Single-Step Adversarial Training},
author={Pau de Jorge and Adel Bibi and Riccardo Volpi and Amartya Sanyal and Philip Torr and Gr{\'e}gory Rogez and Puneet K. Dokania},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=NENo__bExYu}
}
Recently, Wong et al. showed that adversarial training with single-step FGSM leads to a characteristic failure mode named catastrophic overfitting (CO), in which a model becomes suddenly vulnerable to multi-step attacks. They showed that adding a random perturbation prior to FGSM (RS-FGSM) seemed to be sufficient to prevent CO. However, Andriushchenko and Flammarion observed that RS-FGSM still leads to CO for larger perturbations, and proposed an expensive regularizer (GradAlign) to avoid CO. In this work, we methodically revisit the role of noise and clipping in single-step adversarial training. Contrary to previous intuitions, we find that using a stronger noise around the clean sample combined with not clipping is highly effective in avoiding CO for large perturbation radii. Based on these observations, we then propose Noise-FGSM (N-FGSM) that, while providing the benefits of single-step adversarial training, does not suffer from CO. Empirical analyses on a large suite of experiments show that N-FGSM is able to match or surpass the performance of previous single-step methods while achieving a 3× speed-up.
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
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