Paper Info
Title
Two Souls in an Adversarial Image - Towards Universal Adversarial Example Detection using Multi-view Inconsistency.
Abstract
In the evasion attacks against deep neural networks (DNN), the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications. In this paper, we propose a novel multi-view adversarial image detector, namely Argos, based on a novel observation. That is, there exist two "souls" in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the "views") will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that Argos expects to detect. To this end, Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that Argos significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks. Code is available at: https://github.com/sohaib730/Argos-Adversarial_Detection
Year
DOI
Venue
2021
10.1145/3485832.3485904
ACSAC
DocType
ISSN
Citations 
Conference
Annual Computer Security Applications Conference (ACSAC '21), December 6--10, 2021, Virtual Event, USA
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Sohaib Kiani100.68
Sana Awan200.68
Chao Lan312.06
Fengjun Li423323.55
Bo Luo5256.80