Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability

TL;DR

This study investigates how view angle influences the effectiveness of adversarial patches in multi-view settings, revealing that view angle significantly impacts attack success and highlighting the need to consider physical factors in adversarial robustness.

Contribution

It introduces the first multi-view approach combining adversarial patches with perspective transformation to analyze view angle effects on attack efficacy.

Findings

View angle significantly affects adversarial patch effectiveness.

In some cases, patches lose most of their effectiveness due to view changes.

Results suggest incorporating view angle considerations in future adversarial defenses.

Abstract

While machine learning applications are getting mainstream owing to a demonstrated efficiency in solving complex problems, they suffer from inherent vulnerability to adversarial attacks. Adversarial attacks consist of additive noise to an input which can fool a detector. Recently, successful real-world printable adversarial patches were proven efficient against state-of-the-art neural networks. In the transition from digital noise based attacks to real-world physical attacks, the myriad of factors affecting object detection will also affect adversarial patches. Among these factors, view angle is one of the most influential, yet under-explored. In this paper, we study the effect of view angle on the effectiveness of an adversarial patch. To this aim, we propose the first approach that considers a multi-view context by combining existing adversarial patches with a perspective geometric transformation in order to simulate the effect of view angle changes. Our approach has been evaluated on two datasets: the first dataset which contains most real world constraints of a multi-view context, and the second dataset which empirically isolates the effect of view angle. The experiments show that view angle significantly affects the performance of adversarial patches, where in some cases the patch loses most of its effectiveness. We believe that these results motivate taking into account the effect of view angles in future adversarial attacks, and open up new opportunities for adversarial defenses.