CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack

• URL: http://arxiv.org/abs/2506.00978v2
• Date: Mon, 09 Jun 2025 15:25:33 GMT
• Title: CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack
• Authors: Zhan Li, Mingyu Zhao, Xin Dong, Haibin Ling, Bingyao Huang,
• Abstract summary: A projector-based adversarial attack aims to project light patterns onto scenes to deceive deep image classifiers.<n>It has potential applications in privacy protection and the development of more robust classifiers.<n>We introduce weighting-agnostic Projector-Based Adrial Attack (CAPAA) to address these issues.<n>CAPAA achieves both a higher attack success rate and greater stealthiness compared to existing baselines.
• Score: 53.97547369215603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Projector-based adversarial attack aims to project carefully designed light patterns (i.e., adversarial projections) onto scenes to deceive deep image classifiers. It has potential applications in privacy protection and the development of more robust classifiers. However, existing approaches primarily focus on individual classifiers and fixed camera poses, often neglecting the complexities of multi-classifier systems and scenarios with varying camera poses. This limitation reduces their effectiveness when introducing new classifiers or camera poses. In this paper, we introduce Classifier-Agnostic Projector-Based Adversarial Attack (CAPAA) to address these issues. First, we develop a novel classifier-agnostic adversarial loss and optimization framework that aggregates adversarial and stealthiness loss gradients from multiple classifiers. Then, we propose an attention-based gradient weighting mechanism that concentrates perturbations on regions of high classification activation, thereby improving the robustness of adversarial projections when applied to scenes with varying camera poses. Our extensive experimental evaluations demonstrate that CAPAA achieves both a higher attack success rate and greater stealthiness compared to existing baselines. Codes are available at: https://github.com/ZhanLiQxQ/CAPAA.

Related papers

• Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach [26.259289475583522]
  Multi-target adversarial attacks have garnered significant attention due to their ability to generate adversarial images for multiple target classes simultaneously.<n>To address this gap, we first identify and validate that the semantic feature quality and quantity are critical factors affecting the transferability of targeted attacks.<n>We propose the 2D-TGAF framework, which leverages the powerful generative capabilities of diffusion models to encode target labels into two-dimensional semantic tensors.
  arXiv  Detail & Related papers  (2025-04-19T02:08:48Z)
• To Make Yourself Invisible with Adversarial Semantic Contours [47.755808439588094]
  Adversarial Semantic Contour (ASC) is an estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour. We show that ASC can corrupt the prediction of 9 modern detectors with different architectures. We conclude with cautions about contour being the common weakness of object detectors with various architecture.
  arXiv  Detail & Related papers  (2023-03-01T07:22:39Z)
• CARBEN: Composite Adversarial Robustness Benchmark [70.05004034081377]
  This paper demonstrates how composite adversarial attack (CAA) affects the resulting image. It provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level. A leaderboard to benchmark adversarial robustness against CAA is also introduced.
  arXiv  Detail & Related papers  (2022-07-16T01:08:44Z)
• Context-Aware Transfer Attacks for Object Detection [51.65308857232767]
  We present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks.
  arXiv  Detail & Related papers  (2021-12-06T18:26:39Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers [82.19722134082645]
  A stealthy projector-based adversarial attack is proposed in this paper. We approximate the real project-and-capture operation using a deep neural network named PCNet. Our experiments show that the proposed SPAA clearly outperforms other methods by achieving higher attack success rates.
  arXiv  Detail & Related papers  (2020-12-10T18:14:03Z)
• Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity [89.26308254637702]
  We propose a detection strategy to highlight adversarial support sets. We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection. Our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge.
  arXiv  Detail & Related papers  (2020-12-09T14:13:41Z)
• An Evasion Attack against Stacked Capsule Autoencoder [0.0]
  We propose an evasion attack against the Stacked Capsule Autoencoder (SCAE) We evaluate the attack using an image classification experiment, and the experimental results indicate that the attack can achieve high success rates and stealthiness.
  arXiv  Detail & Related papers  (2020-10-14T16:44:10Z)

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.