epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition

• URL: http://arxiv.org/abs/2403.06661v1
• Date: Mon, 11 Mar 2024 12:29:55 GMT
• Title: epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
• Authors: Batuhan Cengiz, Mert Gulsen, Yusuf H. Sahin, Gozde Unal
• Abstract summary: A common method is to use adversarial attacks where the gradient direction is followed to change the input slightly. In this paper, we suggest an adversarial attack called $epsilon$-Mesh Attack, which operates on point cloud data via limiting perturbations to be on the mesh surface. Our method successfully confuses trained DGCNN and PointNet models $99.72%$ and $97.06%$ of the time, with indistinguishable facial deformations.
• Score: 0.8192907805418583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds and meshes are widely used 3D data structures for many computer vision applications. While the meshes represent the surfaces of an object, point cloud represents sampled points from the surface which is also the output of modern sensors such as LiDAR and RGB-D cameras. Due to the wide application area of point clouds and the recent advancements in deep neural networks, studies focusing on robust classification of the 3D point cloud data emerged. To evaluate the robustness of deep classifier networks, a common method is to use adversarial attacks where the gradient direction is followed to change the input slightly. The previous studies on adversarial attacks are generally evaluated on point clouds of daily objects. However, considering 3D faces, these adversarial attacks tend to affect the person's facial structure more than the desired amount and cause malformation. Specifically for facial expressions, even a small adversarial attack can have a significant effect on the face structure. In this paper, we suggest an adversarial attack called $\epsilon$-Mesh Attack, which operates on point cloud data via limiting perturbations to be on the mesh surface. We also parameterize our attack by $\epsilon$ to scale the perturbation mesh. Our surface-based attack has tighter perturbation bounds compared to $L_2$ and $L_\infty$ norm bounded attacks that operate on unit-ball. Even though our method has additional constraints, our experiments on CoMA, Bosphorus and FaceWarehouse datasets show that $\epsilon$-Mesh Attack (Perpendicular) successfully confuses trained DGCNN and PointNet models $99.72\%$ and $97.06\%$ of the time, with indistinguishable facial deformations. The code is available at https://github.com/batuceng/e-mesh-attack.

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