DAFAR: Detecting Adversaries by Feedback-Autoencoder Reconstruction

• URL: http://arxiv.org/abs/2103.06487v1
• Date: Thu, 11 Mar 2021 06:18:50 GMT
• Title: DAFAR: Detecting Adversaries by Feedback-Autoencoder Reconstruction
• Authors: Haowen Liu, Ping Yi, Hsiao-Ying Lin, Jie Shi
• Abstract summary: DAFAR allows deep learning models to detect adversarial examples in high accuracy and universality. It transforms imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder. Experiments show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples.
• Score: 7.867922462470315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has shown impressive performance on challenging perceptual tasks. However, researchers found deep neural networks vulnerable to adversarial examples. Since then, many methods are proposed to defend against or detect adversarial examples, but they are either attack-dependent or shown to be ineffective with new attacks. We propose DAFAR, a feedback framework that allows deep learning models to detect adversarial examples in high accuracy and universality. DAFAR has a relatively simple structure, which contains a target network, a plug-in feedback network and an autoencoder-based detector. The key idea is to capture the high-level features extracted by the target network, and then reconstruct the input using the feedback network. These two parts constitute a feedback autoencoder. It transforms the imperceptible-perturbation attack on the target network directly into obvious reconstruction-error attack on the feedback autoencoder. Finally the detector gives an anomaly score and determines whether the input is adversarial according to the reconstruction errors. Experiments are conducted on MNIST and CIFAR-10 data-sets. Experimental results show that DAFAR is effective against popular and arguably most advanced attacks without losing performance on legitimate samples, with high accuracy and universality across attack methods and parameters.

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