Abstract | ||
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Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. In particular, L-0 AEs are a category of widely discussed threats where adversaries are restricted in the number of pixels that they can corrupt. However, our observation is that, while L-0 attacks modify as few pixels as possible, they tend to cause large-amplitude perturbations to the modified pixels. We consider this as an inherent limitation of L-0 AEs, and thwart such attacks by both detecting and rectifying them. The main novelty of the proposed detector is that we convert the AE detection problem into a comparison problem by exploiting the inherent limitation of L-0 attacks. More concretely, given an image I, it is pre-processed to obtain another image I'. A Siamese network, which is known to be effective in comparison, takes I and I' as the input pair to determine whether I is an AE. A trained Siamese network automatically and precisely captures the discrepancies between I and I' to detect L-0 perturbations. In addition, we show that the pre-processing technique, inpainting, used for detection can also work as an effective defense, which has a high probability of removing the adversarial influence of L-0 perturbations. Thus, our system, called AEPECKER, demonstrates not only high AE detection accuracies, but also a notable capability to correct the classification results. |
Year | Venue | Field |
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2019 | PROCEEDINGS OF THE 22ND INTERNATIONAL SYMPOSIUM ON RESEARCH IN ATTACKS, INTRUSIONS AND DEFENSES | Computer security,Computer science,Adversarial system |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
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Fei Zuo | 1 | 3 | 0.71 |
Bokai Yang | 2 | 0 | 0.34 |
Xiaopeng Li | 3 | 5 | 3.10 |
Zeng Qiang | 4 | 34 | 10.73 |