Related papers: Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

URL: http://arxiv.org/abs/2112.05634v1
Date: Fri, 10 Dec 2021 16:06:03 GMT
Title: Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks
Authors: Seungyong Moon, Gaon An, Hyun Oh Song
Abstract summary: A Man-in-the-Middle adversary maliciously intercepts and perturbs images web users upload online. This type of attack can raise severe ethical concerns on top of simple performance degradation. We devise a novel bi-level optimization algorithm that finds points in the vicinity of natural images that are robust to adversarial perturbations.
Score: 16.017328736786922
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep neural networks have become the driving force of modern image recognition systems. However, the vulnerability of neural networks against adversarial attacks poses a serious threat to the people affected by these systems. In this paper, we focus on a real-world threat model where a Man-in-the-Middle adversary maliciously intercepts and perturbs images web users upload online. This type of attack can raise severe ethical concerns on top of simple performance degradation. To prevent this attack, we devise a novel bi-level optimization algorithm that finds points in the vicinity of natural images that are robust to adversarial perturbations. Experiments on CIFAR-10 and ImageNet show our method can effectively robustify natural images within the given modification budget. We also show the proposed method can improve robustness when jointly used with randomized smoothing.

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