Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness

CVPR 2020 Ahmadreza JeddiMohammad Javad ShafieeMichelle KargChristian ScharfenbergerAlexander Wong

While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widespread deployment for safety-critical applications. Alongside other adversarial defense approaches being investigated, there has been a very recent interest in improving adversarial robustness in deep neural networks through the introduction of perturbations during the training process... (read more)

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