How to Train a Backdoor-Robust Model on a Poisoned Dataset without Auxiliary Data?

• URL: http://arxiv.org/abs/2405.12719v1
• Date: Tue, 21 May 2024 12:20:19 GMT
• Title: How to Train a Backdoor-Robust Model on a Poisoned Dataset without Auxiliary Data?
• Authors: Yuwen Pu, Jiahao Chen, Chunyi Zhou, Zhou Feng, Qingming Li, Chunqiang Hu, Shouling Ji,
• Abstract summary: Backdoor attacks have attracted wide attention from academia and industry due to their great security threat to deep neural networks (DNN) Most of the existing methods propose to conduct backdoor attacks by poisoning the training dataset with different strategies. We introduce AdvrBD, an Adversarial perturbation-based and robust Backdoor Defense framework, which can effectively identify the poisoned samples and train a clean model on the poisoned dataset.
• Score: 29.842087372804905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attacks have attracted wide attention from academia and industry due to their great security threat to deep neural networks (DNN). Most of the existing methods propose to conduct backdoor attacks by poisoning the training dataset with different strategies, so it's critical to identify the poisoned samples and then train a clean model on the unreliable dataset in the context of defending backdoor attacks. Although numerous backdoor countermeasure researches are proposed, their inherent weaknesses render them limited in practical scenarios, such as the requirement of enough clean samples, unstable defense performance under various attack conditions, poor defense performance against adaptive attacks, and so on.Therefore, in this paper, we are committed to overcome the above limitations and propose a more practical backdoor defense method. Concretely, we first explore the inherent relationship between the potential perturbations and the backdoor trigger, and the theoretical analysis and experimental results demonstrate that the poisoned samples perform more robustness to perturbation than the clean ones. Then, based on our key explorations, we introduce AdvrBD, an Adversarial perturbation-based and robust Backdoor Defense framework, which can effectively identify the poisoned samples and train a clean model on the poisoned dataset. Constructively, our AdvrBD eliminates the requirement for any clean samples or knowledge about the poisoned dataset (e.g., poisoning ratio), which significantly improves the practicality in real-world scenarios.

Related papers

• Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models [26.5039481643457]
  We introduce a novel data extraction attack that compromises even exact unlearning.<n>We demonstrate our attack's effectiveness on a simulated medical diagnosis dataset.
  arXiv  Detail & Related papers  (2025-05-30T09:09:33Z)
• Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets [16.619809695639027]
  Pre-training and downstream adaptation expose models to sophisticated backdoor embeddings at both the encoder and dataset levels. In this work, we investigate how to mitigate potential backdoor risks in resource-constrained transfer learning scenarios. We propose the Trusted Core (T-Core) Bootstrapping framework, which emphasizes the importance of pinpointing trustworthy data and neurons to enhance model security.
  arXiv  Detail & Related papers  (2025-04-16T11:33:03Z)
• A Practical Trigger-Free Backdoor Attack on Neural Networks [33.426207982772226]
  We propose a trigger-free backdoor attack that does not require access to any training data. Specifically, we design a novel fine-tuning approach that incorporates the concept of malicious data into the concept of the attacker-specified class. The effectiveness, practicality, and stealthiness of the proposed attack are evaluated on three real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T08:53:36Z)
• Unlearning Backdoor Attacks through Gradient-Based Model Pruning [10.801476967873173]
  We propose a novel approach to counter backdoor attacks by treating their mitigation as an unlearning task. Our approach offers simplicity and effectiveness, rendering it well-suited for scenarios with limited data availability.
  arXiv  Detail & Related papers  (2024-05-07T00:36:56Z)
• Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• 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)
• Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data [26.551317580666353]
  Backdoor attacks pose a serious security threat for training neural networks. We propose a novel approach that enables model training on potentially poisoned datasets by utilizing the power of recent diffusion models.
  arXiv  Detail & Related papers  (2023-10-10T07:25:06Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Data Poisoning Attack Aiming the Vulnerability of Continual Learning [25.480762565632332]
  We present a simple task-specific data poisoning attack that can be used in the learning process of a new task. We experiment with the attack on the two representative regularization-based continual learning methods.
  arXiv  Detail & Related papers  (2022-11-29T02:28:05Z)
• A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks [72.7373468905418]
  We develop an open-source toolkit OpenBackdoor to foster the implementations and evaluations of textual backdoor learning. We also propose CUBE, a simple yet strong clustering-based defense baseline.
  arXiv  Detail & Related papers  (2022-06-17T02:29:23Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)

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.