Denoising by Decorated Noise: An Interpretability-Based Framework for Adversarial Example Detection

The intelligent imaging sensors in IoT benefit a lot from the continuous renewal of deep neural networks (DNNs). However, the appearance of adversarial examples leads to skepticism about the trustworthiness of DNNs. Malicious perturbations, even unperceivable for humans, lead to incapacitations of a DNN, bringing about the security problem in the information integration of an IoT system. Adversarial example detection is an intuitive solution to judge if an input is malicious before acceptance. However, the existing detection approaches, more or less, have some shortcomings like (1) modifying the network structure, (2) extra training before deployment, and (3) requiring some prior knowledge about attacks. To address these problems, this paper proposes a novel framework to filter out the adversarial perturbations by superimposing the original images with the noises decorated by a new gradient-independent visualization method, namely, score class activation map (Score-CAM). We propose to trim the Gaussian noises in a way with more explicit semantic meaning and stronger explainability, which is different from the previous studies based on intuitive hypotheses or artificial denoisers. Our framework requires no extra training and gradient calculation, which is friendly to embedded devices with only inference capabilities. Extensive experiments demonstrate that the proposed framework is sufficiently general to detect a wide range of attacks and apply it to different models.