Simultaneous Adversarial Training - Learn from Others Mistakes
Adversarial examples are maliciously tweaked images that can easily fool machine learning techniques, such as neural networks, but they are normally not visually distinguishable for human beings. One of the main approaches to solve this problem is to retrain the networks using those adversarial examples, namely adversarial training. However, standard adversarial training might not actually change the decision boundaries but cause the problem of gradient masking, resulting in a weaker ability to generate adversarial examples. Therefore, it cannot alleviate the problem of black-box attacks, where adversarial examples generated from other networks can transfer to the targeted one. In order to reduce the problem of black-box attacks, we propose a novel method that allows two networks to learn from each others' adversarial examples and become resilient to black-box attacks. We also combine this method with a simple domain adaptation to further improve the performance.
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Zukang Liao (add twitter)
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07/23/18 06:56PM
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arxiv_cscv: Simultaneous Adversarial Training - Learn from Others Mistakes https://t.co/zv1f5MHvVA
luoyuchu: RT @arxiv_cscv: Simultaneous Adversarial Training - Learn from Others Mistakes https://t.co/zv1f5MpV42
arxiv_cscv: Simultaneous Adversarial Training - Learn from Others Mistakes https://t.co/zv1f5MpV42
nmfeeds: [CV] https://t.co/5kry0zzeuf Simultaneous Adversarial Training - Learn from Others Mistakes. Adversarial examples are mali...
BrundageBot: Simultaneous Adversarial Training - Learn from Others Mistakes. Zukang Liao https://t.co/iVHA05RJR9
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