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
this https URL

基于点云数据的 epsilon-Mesh 攻击方法对训练过的 DGCNN 和 PointNet 模型成功地产生了无法区分的面部变形，攻击成功率分别达到 99.72% 和 97.06%。

epsilon-Mesh 攻击：面向面部表情识别的基于表面的对抗性点云攻击

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

A lot of work has been done towards reconstructing the 3D facial structure
from single images by capitalizing on the power of Deep Convolutional Neural
Networks (DCNNs). In the recent works, the texture features either correspond
to components of a linear texture space or are learned by auto-encoders
directly from in-the-wild images. In all cases, the quality of the facial
texture reconstruction is still not capable of modeling facial texture with
high-frequency details. In this paper, we take a radically different approach
and harness the power of Generative Adversarial Networks (GANs) and DCNNs in
order to reconstruct the facial texture and shape from single images. That is,
we utilize GANs to train a very powerful facial texture prior \edit{from a
large-scale 3D texture dataset}. Then, we revisit the original 3D Morphable
Models (3DMMs) fitting making use of non-linear optimization to find the
optimal latent parameters that best reconstruct the test image but under a new
perspective. In order to be robust towards initialisation and expedite the
fitting process, we propose a novel self-supervised regression based approach.
We demonstrate excellent results in photorealistic and identity preserving 3D
face reconstructions and achieve for the first time, to the best of our
knowledge, facial texture reconstruction with high-frequency details.

本文提出了一种使用深度卷积神经网络和生成对抗网络重建人脸纹理和形状的新方法，以及一种自监督回归方法用于较快的拟合过程，展示了可生成高频细节的写实和身份保留的 3D 面部重建。