TPAM: Transferable Perceptual-constrained Adversarial Meshes
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Triangle meshes are widely used in 3D data representation due to their efficacy in capturing complex surfaces. Mesh classification, crucial in various applications, has typically been tackled by Deep Neural Networks (DNNs) with advancements in deep learning. However, these mesh networks have been proven vulnerable to adversarial attacks, where slight distortions to meshes can cause large prediction errors, posing significant security risks. Although several mesh attack methods have been proposed recently, two key aspects of Stealthiness and Transferability remain underexplored. This paper introduces a new method called Transferable Perceptual-constrained Adversarial Meshes (TPAM) to investigate these aspects in adversarial attacks further. Specifically, we present a Perceptual-constrained objective term to restrict the distortions and introduce an Adaptive Geometry-aware Attack Optimization strategy to adjust attacking strength iteratively based on local geometric frequencies, striking a good balance between stealthiness and attacking accuracy. Moreover, we propose a Bayesian Surrogate Network to enhance transferability and introduce a new metric, the Area Under Accuracy (AUACC), for comprehensive performance evaluation. Experiments on various mesh classifiers demonstrate the effectiveness of our method in both white-box and black-box settings, enhancing the attack stealthiness and transferability across multiple networks. Our research can enhance the understanding of DNNs, thus improving the robustness of mesh classifiers. The code is available at https://github.com/Tengjia-Kang/TPAM.
Description
CCS Concepts: Computing methodologies → Mesh geometry models; Shape analysis
@inproceedings{10.2312:pg.20241285,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{TPAM: Transferable Perceptual-constrained Adversarial Meshes}},
author = {Kang, Tengjia and Li, Yuezun and Zhou, Jiaran and Xin, Shiqing and Dong, Junyu and Tu, Changhe},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241285}
}