Can Targeted Clean-Label Poisoning Attacks Generalize?

• URL: http://arxiv.org/abs/2412.03908v1
• Date: Thu, 05 Dec 2024 06:27:14 GMT
• Title: Can Targeted Clean-Label Poisoning Attacks Generalize?
• Authors: Zhizhen Chen, Subrat Kishore Dutta, Zhengyu Zhao, Chenhao Lin, Chao Shen, Xiao Zhang,
• Abstract summary: We study whether targeted poisoning attacks can generalize to unknown variations of those targets.<n>In particular, we explore diverse target variations, such as an object with varied viewpoints and an animal species with distinct appearances.<n>Our method outperforms the cosine similarity-based attack by 20.95% in attack success rate with similar overall accuracy.
• Score: 11.499065606209925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Targeted poisoning attacks aim to compromise the model's prediction on specific target samples. In a common clean-label setting, they are achieved by slightly perturbing a subset of training samples given access to those specific targets. Despite continuous efforts, it remains unexplored whether such attacks can generalize to unknown variations of those targets. In this paper, we take the first step to systematically study this generalization problem. Observing that the widely adopted, cosine similarity-based attack exhibits limited generalizability, we propose a well-generalizable attack that leverages both the direction and magnitude of model gradients. In particular, we explore diverse target variations, such as an object with varied viewpoints and an animal species with distinct appearances. Extensive experiments across various generalization scenarios demonstrate that our method consistently achieves the best attack effectiveness. For example, our method outperforms the cosine similarity-based attack by 20.95% in attack success rate with similar overall accuracy, averaged over four models on two image benchmark datasets. The code is available at https://github.com/jiaangk/generalizable_tcpa

Related papers

• Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection [83.72430401516674]
  GAKer is able to construct adversarial examples to any target class. Our method achieves an approximately $14.13%$ higher attack success rate for unknown classes.
  arXiv  Detail & Related papers  (2024-07-17T03:24:09Z)
• AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples [26.37278338032268]
  Adrial examples are typically optimized with gradient-based attacks. Each is shown to outperform its predecessors using different experimental setups. This provides overly-optimistic and even biased evaluations.
  arXiv  Detail & Related papers  (2024-04-30T11:19:05Z)
• Can We Trust the Unlabeled Target Data? Towards Backdoor Attack and Defense on Model Adaptation [120.42853706967188]
  We explore the potential backdoor attacks on model adaptation launched by well-designed poisoning target data. We propose a plug-and-play method named MixAdapt, combining it with existing adaptation algorithms.
  arXiv  Detail & Related papers  (2024-01-11T16:42:10Z)
• Transferable Attack for Semantic Segmentation [59.17710830038692]
  adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target models. We propose an ensemble attack for semantic segmentation to achieve more effective attacks with higher transferability.
  arXiv  Detail & Related papers  (2023-07-31T11:05:55Z)
• GLOW: Global Layout Aware Attacks for Object Detection [27.46902978168904]
  Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. We present first approach that copes with various attack requests by generating global layout-aware adversarial attacks. In experiment, we design multiple types of attack requests and validate our ideas on MS validation set.
  arXiv  Detail & Related papers  (2023-02-27T22:01:34Z)
• Object-fabrication Targeted Attack for Object Detection [54.10697546734503]
  adversarial attack for object detection contains targeted attack and untargeted attack. New object-fabrication targeted attack mode can mislead detectors tofabricate extra false objects with specific target labels.
  arXiv  Detail & Related papers  (2022-12-13T08:42:39Z)
• Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation [11.663072799764542]
  This work proposes the task of target identification, which determines whether a specific test instance is the target of a training-set attack. Rather than focusing on a single attack method or data modality, we build on influence estimation, which quantifies each training instance's contribution to a model's prediction.
  arXiv  Detail & Related papers  (2022-01-25T02:36:34Z)
• 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)
• Hidden Backdoor Attack against Semantic Segmentation Models [60.0327238844584]
  The emphbackdoor attack intends to embed hidden backdoors in deep neural networks (DNNs) by poisoning training data. We propose a novel attack paradigm, the emphfine-grained attack, where we treat the target label from the object-level instead of the image-level. Experiments show that the proposed methods can successfully attack semantic segmentation models by poisoning only a small proportion of training data.
  arXiv  Detail & Related papers  (2021-03-06T05:50:29Z)
• Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series [0.0]
  We have performed untargeted, targeted and universal adversarial attacks on UCR time series datasets. Our results show that deep learning based time series classification models are vulnerable to these attacks. We also show that universal adversarial attacks have good generalization property as it need only a fraction of the training data.
  arXiv  Detail & Related papers  (2021-01-13T13:00:51Z)

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.