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Deep Defense: Training DNNs with Improved Adversarial Robustness
[article]
2018
arXiv
pre-print
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial
arXiv:1803.00404v3
fatcat:kqo5qp5zkfdrhmenqitkjkljv4