# Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks Source: [Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:48:06.915Z Authors: Seungyong Moon, Gaon An, Hyun Oh Song Published: 2021-12-10T16:06:03.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()