FFCBA: Feature-based Full-target Clean-label Backdoor Attacks

• URL: http://arxiv.org/abs/2504.21054v1
• Date: Tue, 29 Apr 2025 05:49:42 GMT
• Title: FFCBA: Feature-based Full-target Clean-label Backdoor Attacks
• Authors: Yangxu Yin, Honglong Chen, Yudong Gao, Peng Sun, Liantao Wu, Zhe Li, Weifeng Liu,
• Abstract summary: Backdoor attacks pose a significant threat to deep neural networks.<n>Clean-label attacks are more stealthy, as they avoid modifying the labels of poisoned samples.<n>We propose the Feature-based Full-target Clean-label Backdoor Attacks (FFCBA)
• Score: 10.650796825194337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples. Among these, multi-target backdoor attacks can simultaneously target multiple classes. However, existing multi-target backdoor attacks all follow the dirty-label paradigm, where poisoned samples are mislabeled, and most of them require an extremely high poisoning rate. This makes them easily detectable by manual inspection. In contrast, clean-label attacks are more stealthy, as they avoid modifying the labels of poisoned samples. However, they generally struggle to achieve stable and satisfactory attack performance and often fail to scale effectively to multi-target attacks. To address this issue, we propose the Feature-based Full-target Clean-label Backdoor Attacks (FFCBA) which consists of two paradigms: Feature-Spanning Backdoor Attacks (FSBA) and Feature-Migrating Backdoor Attacks (FMBA). FSBA leverages class-conditional autoencoders to generate noise triggers that align perturbed in-class samples with the original category's features, ensuring the effectiveness, intra-class consistency, inter-class specificity and natural-feature correlation of triggers. While FSBA supports swift and efficient attacks, its cross-model attack capability is relatively weak. FMBA employs a two-stage class-conditional autoencoder training process that alternates between using out-of-class samples and in-class samples. This allows FMBA to generate triggers with strong target-class features, making it highly effective for cross-model attacks. We conduct experiments on multiple datasets and models, the results show that FFCBA achieves outstanding attack performance and maintains desirable robustness against the state-of-the-art backdoor defenses.

Related papers

• SFIBA: Spatial-based Full-target Invisible Backdoor Attacks [9.124060365358748]
  Multi-target backdoor attacks pose significant security threats to deep neural networks.<n>We propose a Spatial-based Full-target Invisible Backdoor Attack, called SFIBA.<n>We show that SFIBA can achieve excellent attack performance and stealthiness, while preserving the model's performance on benign samples.
  arXiv  Detail & Related papers  (2025-04-29T05:28:12Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks. $textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning. $textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• NoiseAttack: An Evasive Sample-Specific Multi-Targeted Backdoor Attack Through White Gaussian Noise [0.19820694575112383]
  Backdoor attacks pose a significant threat when using third-party data for deep learning development. We introduce a novel sample-specific multi-targeted backdoor attack, namely NoiseAttack. This work is the first of its kind to launch a vision backdoor attack with the intent to generate multiple targeted classes.
  arXiv  Detail & Related papers  (2024-09-03T19:24:46Z)
• EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection [53.25863925815954]
  Federated self-supervised learning (FSSL) has emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data. While FSSL offers advantages, its susceptibility to backdoor attacks has not been investigated. We propose the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models.
  arXiv  Detail & Related papers  (2024-05-21T06:14:49Z)
• Protecting Model Adaptation from Trojans in the Unlabeled Data [120.42853706967188]
  This paper explores the potential trojan attacks on model adaptation launched by well-designed poisoning target data.<n>We propose a plug-and-play method named DiffAdapt, which can be seamlessly integrated with existing adaptation algorithms.
  arXiv  Detail & Related papers  (2024-01-11T16:42:10Z)
• Does Few-shot Learning Suffer from Backdoor Attacks? [63.9864247424967]
  We show that few-shot learning can still be vulnerable to backdoor attacks. Our method demonstrates a high Attack Success Rate (ASR) in FSL tasks with different few-shot learning paradigms. This study reveals that few-shot learning still suffers from backdoor attacks, and its security should be given attention.
  arXiv  Detail & Related papers  (2023-12-31T06:43:36Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger [57.48983433438498]
  We show that sample-specific backdoor attacks ( SSBAs) are not sufficiently stealthy due to their poisoned-label nature.<n>We propose to exploit content-relevant features, $a.k.a.$ (human-relied) attributes, as the trigger patterns to design clean-label SSBAs.
  arXiv  Detail & Related papers  (2023-12-03T09:12:14Z)
• Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class [17.391987602738606]
  In recent years, machine learning models have been shown to be vulnerable to backdoor attacks. This paper exploits a novel backdoor attack with a much more powerful payload, denoted as Marksman. We show empirically that the proposed framework achieves high attack performance while preserving the clean-data performance in several benchmark datasets.
  arXiv  Detail & Related papers  (2022-10-17T15:46:57Z)
• Under-confidence Backdoors Are Resilient and Stealthy Backdoors [35.57996363193643]
  backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designed backdoors. In order to achieve a high attack success rate, most existing attack methods change the labels of the poisoned samples to the target class. This practice often results in severe over-fitting of the victim model over the backdoors, making the attack quite effective in output control but easier to be identified by human inspection or automatic defense algorithms.
  arXiv  Detail & Related papers  (2022-02-19T01:31:41Z)
• Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios [22.22337220509128]
  Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. We propose a detection framework based on BP reverse-engineering and a novel it expected transferability (ET) statistic.
  arXiv  Detail & Related papers  (2022-01-20T22:21:38Z)
• 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)

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.