A Physical-World Adversarial Attack Against 3D Face Recognition

• URL: http://arxiv.org/abs/2205.13412v1
• Date: Thu, 26 May 2022 15:06:14 GMT
• Title: A Physical-World Adversarial Attack Against 3D Face Recognition
• Authors: Yanjie Li, Yiquan Li, Bin Xiao
• Abstract summary: structured light imaging is a common method to measure the 3D shape. This method could be easily attacked, leading to inaccurate 3D face recognition. We propose a novel, physically-achievable attack on the fringe structured light system, named structured light attack.
• Score: 10.577749566854626
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D face recognition systems have been widely employed in intelligent terminals, among which structured light imaging is a common method to measure the 3D shape. However, this method could be easily attacked, leading to inaccurate 3D face recognition. In this paper, we propose a novel, physically-achievable attack on the fringe structured light system, named structured light attack. The attack utilizes a projector to project optical adversarial fringes on faces to generate point clouds with well-designed noises. We firstly propose a 3D transform-invariant loss function to enhance the robustness of 3D adversarial examples in the physical-world attack. Then we reverse the 3D adversarial examples to the projector's input to place noises on phase-shift images, which models the process of structured light imaging. A real-world structured light system is constructed for the attack and several state-of-the-art 3D face recognition neural networks are tested. Experiments show that our method can attack the physical system successfully and only needs minor modifications of projected images.

Related papers

• VoxAtnNet: A 3D Point Clouds Convolutional Neural Network for Generalizable Face Presentation Attack Detection [2.6118211807973157]
  Face biometric systems are vulnerable to Presentation Attacks (PAs) We propose a novel Presentation Attack Detection (PAD) algorithm based on 3D point clouds captured using the frontal camera of a smartphone.
  arXiv  Detail & Related papers  (2024-04-19T07:30:36Z)
• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• A Perceptual Shape Loss for Monocular 3D Face Reconstruction [13.527078921914985]
  We propose a new loss function for monocular face capture inspired by how humans would perceive the quality of a 3D face reconstruction. Our loss is implemented as a discriminator-style neural network that takes an input face image and a shaded render of the geometry estimate. We show how our new perceptual shape loss can be combined with traditional energy terms for monocular 3D face optimization and deep neural network regression.
  arXiv  Detail & Related papers  (2023-10-30T14:39:11Z)
• Isometric 3D Adversarial Examples in the Physical World [34.291370103424995]
  3D deep learning models are shown to be as vulnerable to adversarial examples as 2D models. Existing attack methods are still far from stealthy and suffer from severe performance degradation in the physical world. We propose a novel $epsilon$-isometric ($epsilon$-ISO) attack to generate natural and robust 3D adversarial examples.
  arXiv  Detail & Related papers  (2022-10-27T09:58:15Z)
• Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape [77.95154911528365]
  3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. Previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry. This paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person.
  arXiv  Detail & Related papers  (2022-04-09T03:46:18Z)
• 3D Face Morphing Attacks: Generation, Vulnerability and Detection [3.700129710233692]
  Face Recognition systems have been found to be vulnerable to morphing attacks. This work presents a novel direction for generating face-morphing attacks in 3D.
  arXiv  Detail & Related papers  (2022-01-10T16:53:39Z)
• Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting [149.1673041605155]
  We address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. We propose a unified, learning-based inverse framework that formulates 3D spatially-varying lighting.
  arXiv  Detail & Related papers  (2021-09-13T15:29:03Z)
• Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving [87.3492357041748]
  In this paper, we showcase practical susceptibilities of multi-sensor detection by placing an adversarial object on top of a host vehicle. Our experiments demonstrate that successful attacks are primarily caused by easily corrupted image features. Towards more robust multi-modal perception systems, we show that adversarial training with feature denoising can boost robustness to such attacks significantly.
  arXiv  Detail & Related papers  (2021-01-17T21:15:34Z)
• DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation [56.56575063461169]
  DeepFaceFlow is a robust, fast, and highly-accurate framework for the estimation of 3D non-rigid facial flow. Our framework was trained and tested on two very large-scale facial video datasets. Given registered pairs of images, our framework generates 3D flow maps at 60 fps.
  arXiv  Detail & Related papers  (2020-05-14T23:56:48Z)
• 3D Face Anti-spoofing with Factorized Bilinear Coding [35.30886962572515]
  We propose a novel anti-spoofing method from the perspective of fine-grained classification. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection.
  arXiv  Detail & Related papers  (2020-05-12T03:09:20Z)
• Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method [90.26041504667451]
  We show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features. The proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.
  arXiv  Detail & Related papers  (2020-04-03T20:17:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.