A Direct Approach to Robust Deep Learning Using Adversarial Networks
This addresses the problem of adversarial robustness in deep learning for image classification, but it is incremental as it builds on existing GAN-based approaches.
The paper tackles the vulnerability of deep neural networks to adversarial examples by proposing a new defensive mechanism using a generative adversarial network (GAN) framework, showing empirically that it performs on par with state-of-the-art methods against black box attacks.
Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network (GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.