Confidence-driven Sampling for Backdoor Attacks

• URL: http://arxiv.org/abs/2310.05263v1
• Date: Sun, 8 Oct 2023 18:57:36 GMT
• Title: Confidence-driven Sampling for Backdoor Attacks
• Authors: Pengfei He, Han Xu, Yue Xing, Jie Ren, Yingqian Cui, Shenglai Zeng, Jiliang Tang, Makoto Yamada, Mohammad Sabokrou,
• Abstract summary: Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. We introduce a straightforward yet highly effective sampling methodology that leverages confidence scores. Specifically, it selects samples with lower confidence scores, significantly increasing the challenge for defenders in identifying and countering these attacks.
• Score: 49.72680157684523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. Our research highlights the overlooked drawbacks of random sampling, which make that attack detectable and defensible. The core idea of this paper is to strategically poison samples near the model's decision boundary and increase defense difficulty. We introduce a straightforward yet highly effective sampling methodology that leverages confidence scores. Specifically, it selects samples with lower confidence scores, significantly increasing the challenge for defenders in identifying and countering these attacks. Importantly, our method operates independently of existing trigger designs, providing versatility and compatibility with various backdoor attack techniques. We substantiate the effectiveness of our approach through a comprehensive set of empirical experiments, demonstrating its potential to significantly enhance resilience against backdoor attacks in DNNs.

Related papers

• DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks [30.766013737094532]
  We propose DMGNN against out-of-distribution (OOD) and in-distribution (ID) graph backdoor attacks. DMGNN can easily identify the hidden ID and OOD triggers via predicting label transitions based on counterfactual explanation. DMGNN far outperforms the state-of-the-art (SOTA) defense methods, reducing the attack success rate to 5% with almost negligible degradation in model performance.
  arXiv  Detail & Related papers  (2024-10-18T01:08:03Z)
• SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks [53.28390057407576]
  Modern NLP models are often trained on public datasets drawn from diverse sources. Data poisoning attacks can manipulate the model's behavior in ways engineered by the attacker. Several strategies have been proposed to mitigate the risks associated with backdoor attacks.
  arXiv  Detail & Related papers  (2024-05-19T14:50:09Z)
• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning [49.242828934501986]
  Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features. backdoor attacks subtly embed malicious behaviors within the model during training. We introduce an innovative token-based localized forgetting training regime.
  arXiv  Detail & Related papers  (2024-03-24T18:33:15Z)
• Backdoor Attack with Sparse and Invisible Trigger [57.41876708712008]
  Deep neural networks (DNNs) are vulnerable to backdoor attacks. backdoor attack is an emerging yet threatening training-phase threat. We propose a sparse and invisible backdoor attack (SIBA)
  arXiv  Detail & Related papers  (2023-05-11T10:05:57Z)
• SATBA: An Invisible Backdoor Attack Based On Spatial Attention [7.405457329942725]
  Backdoor attacks involve the training of Deep Neural Network (DNN) on datasets that contain hidden trigger patterns. Most existing backdoor attacks suffer from two significant drawbacks: their trigger patterns are visible and easy to detect by backdoor defense or even human inspection. We propose a novel backdoor attack named SATBA that overcomes these limitations using spatial attention and an U-net based model.
  arXiv  Detail & Related papers  (2023-02-25T10:57:41Z)
• Untargeted Backdoor Attack against Object Detection [69.63097724439886]
  We design a poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns.
  arXiv  Detail & Related papers  (2022-11-02T17:05:45Z)
• Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization [17.756085566366167]
  We propose a feature randomization-based approach that resists eight adversarial attacks targeting deep learning models. Our methodology can secure the target network and resists adversarial attack transferability by over 60%.
  arXiv  Detail & Related papers  (2022-09-11T20:14:12Z)

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.