Adversarial training via projected gradient descent on the inner maximization problem produces neural networks with substantially improved resistance to a wide range of attacks and establishes security against first-order adversaries as a concrete guarantee.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Towards Deep Learning Models Resistant to Adversarial Attacks
Adversarial training via projected gradient descent on the inner maximization problem produces neural networks with substantially improved resistance to a wide range of attacks and establishes security against first-order adversaries as a concrete guarantee.