Deep Neural Rejection against Adversarial Examples
This work addresses the security problem of adversarial attacks for users of deep learning systems, presenting an incremental improvement over existing detection methods.
The paper tackles the vulnerability of deep neural networks to adversarial examples by proposing a deep neural rejection mechanism that detects such samples based on anomalous feature representations across network layers, and shows it outperforms previous methods under a worst-case adaptive white-box attack.
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time. In this work, we propose a deep neural rejection mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. With respect to competing approaches, our method does not require generating adversarial examples at training time, and it is less computationally demanding. To properly evaluate our method, we define an adaptive white-box attack that is aware of the defense mechanism and aims to bypass it. Under this worst-case setting, we empirically show that our approach outperforms previously-proposed methods that detect adversarial examples by only analyzing the feature representation provided by the output network layer.