One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation

• URL: http://arxiv.org/abs/2404.02287v1
• Date: Tue, 2 Apr 2024 20:29:59 GMT
• Title: One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
• Authors: Mehmet Ergezer, Phat Duong, Christian Green, Tommy Nguyen, Abdurrahman Zeybey,
• Abstract summary: This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on multiple 2D images, offering a practical and scalable solution.
• Score: 1.4680035572775534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on multiple 2D images, offering a practical and scalable solution for enhancing model scalability and robustness. This generalizable method bridges the gap between 2D perturbations and 3D-like attack capabilities, making it suitable for real-world applications. Existing adversarial attacks may become ineffective when images undergo transformations like changes in lighting, camera position, or natural deformations. We address this challenge by crafting a single universal noise perturbation applicable to various object views. Experiments on diverse rendered 3D objects demonstrate the effectiveness of our approach. The universal perturbation successfully identified a single adversarial noise for each given set of 3D object renders from multiple poses and viewpoints. Compared to single-view attacks, our universal attacks lower classification confidence across multiple viewing angles, especially at low noise levels. A sample implementation is made available at https://github.com/memoatwit/UniversalPerturbation.

Related papers

• Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition [1.7205106391379021]
  Adversarial attacks pose significant challenges in 3D object recognition. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples. We demonstrate the effectiveness of VIAP in both targeted and untargeted settings.
  arXiv  Detail & Related papers  (2024-12-17T23:23:25Z)
• DVPE: Divided View Position Embedding for Multi-View 3D Object Detection [7.791229698270439]
  Current research faces challenges in balancing between receptive fields and reducing interference when aggregating multi-view features. This paper proposes a divided view method, in which features are modeled globally via the visibility crossattention mechanism, but interact only with partial features in a divided local virtual space. Our framework, named DVPE, achieves state-of-the-art performance (57.2% mAP and 64.5% NDS) on the nuScenes test set.
  arXiv  Detail & Related papers  (2024-07-24T02:44:41Z)
• Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing [28.874014617259935]
  Multi-Camera 3D Object Detection (MC3D-Det) has gained prominence with the advent of bird's-eye view (BEV) approaches. We propose a novel method that aligns 3D detection with 2D camera plane results, ensuring consistent and accurate detections.
  arXiv  Detail & Related papers  (2023-10-17T15:31:28Z)
• Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild [10.849750765175754]
  POTR-3D is a sequence-to-sequence 2D-to-3D lifting model for 3DMPPE. It robustly generalizes to diverse unseen views, robustly recovers the poses against heavy occlusions, and reliably generates more natural and smoother outputs.
  arXiv  Detail & Related papers  (2023-09-15T06:17:22Z)
• AnyDoor: Zero-shot Object-level Image Customization [63.44307304097742]
  This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations. Our model is trained only once and effortlessly generalizes to diverse object-scene combinations at the inference stage.
  arXiv  Detail & Related papers  (2023-07-18T17:59:02Z)
• Multiview Compressive Coding for 3D Reconstruction [77.95706553743626]
  We introduce a simple framework that operates on 3D points of single objects or whole scenes. Our model, Multiview Compressive Coding, learns to compress the input appearance and geometry to predict the 3D structure.
  arXiv  Detail & Related papers  (2023-01-19T18:59:52Z)
• Progressive Multi-view Human Mesh Recovery with Self-Supervision [68.60019434498703]
  Existing solutions typically suffer from poor generalization performance to new settings. We propose a novel simulation-based training pipeline for multi-view human mesh recovery.
  arXiv  Detail & Related papers  (2022-12-10T06:28:29Z)
• Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks [48.66027897216473]
  We tackle a more practical problem of generating adversarial perturbations using multi-object (i.e., multiple dominant objects) images. We propose a novel generative attack (called Local Patch Difference or LPD-Attack) where a novel contrastive loss function uses the aforesaid local differences in feature space of multi-object scenes. Our approach outperforms baseline generative attacks with highly transferable perturbations when evaluated under different white-box and black-box settings.
  arXiv  Detail & Related papers  (2022-09-20T17:36:32Z)
• Vision Transformer for NeRF-Based View Synthesis from a Single Input Image [49.956005709863355]
  We propose to leverage both the global and local features to form an expressive 3D representation. To synthesize a novel view, we train a multilayer perceptron (MLP) network conditioned on the learned 3D representation to perform volume rendering. Our method can render novel views from only a single input image and generalize across multiple object categories using a single model.
  arXiv  Detail & Related papers  (2022-07-12T17:52:04Z)
• 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)

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.