Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning

• URL: http://arxiv.org/abs/2404.13946v1
• Date: Mon, 22 Apr 2024 07:44:02 GMT
• Title: Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning
• Authors: Rong Wang, Guichen Zhou, Mingjun Gao, Yunpeng Xiao,
• Abstract summary: This paper designs a backdoor attack method based on federated learning. aiming at the concealment of the backdoor trigger, a TrojanGan steganography model with encoder-decoder structure is designed. A dual model replacement backdoor attack algorithm based on federated learning is designed.
• Score: 21.600003684064706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the neural network backdoor hidden in the parameters of the federated learning model has been proved to have great security risks. Considering the characteristics of trigger generation, data poisoning and model training in backdoor attack, this paper designs a backdoor attack method based on federated learning. Firstly, aiming at the concealment of the backdoor trigger, a TrojanGan steganography model with encoder-decoder structure is designed. The model can encode specific attack information as invisible noise and attach it to the image as a backdoor trigger, which improves the concealment and data transformations of the backdoor trigger.Secondly, aiming at the problem of single backdoor trigger mode, an image poisoning attack method called combination trigger attack is proposed. This method realizes multi-backdoor triggering by multiplexing combined triggers and improves the robustness of backdoor attacks. Finally, aiming at the problem that the local training mechanism leads to the decrease of the success rate of backdoor attack, a dual model replacement backdoor attack algorithm based on federated learning is designed. This method can improve the success rate of backdoor attack while maintaining the performance of the federated learning aggregation model. Experiments show that the attack strategy in this paper can not only achieve high backdoor concealment and diversification of trigger forms under federated learning, but also achieve good attack success rate in multi-target attacks.door concealment and diversification of trigger forms but also achieve good results in multi-target attacks.

Related papers

• Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning [5.91728247370845]
  Federated learning is vulnerable to backdoor attacks due to its distributed nature. We propose a more practical threat model for federated learning: the distributed multi-target backdoor. We show that 30 rounds after the attack, Attack Success rates of three different backdoors from various clients remain above 93%.
  arXiv  Detail & Related papers  (2024-11-06T13:57:53Z)
• Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats [52.94388672185062]
  We propose an efficient defense mechanism against backdoor threats using a concept known as machine unlearning. This entails strategically creating a small set of poisoned samples to aid the model's rapid unlearning of backdoor vulnerabilities. In the backdoor unlearning process, we present a novel token-based portion unlearning training regime.
  arXiv  Detail & Related papers  (2024-09-29T02:55:38Z)
• PureDiffusion: Using Backdoor to Counter Backdoor in Generative Diffusion Models [5.957580737396457]
  Diffusion models (DMs) are advanced deep learning models that achieved state-of-the-art capability on a wide range of generative tasks. Recent studies have shown their vulnerability regarding backdoor attacks, in which backdoored DMs consistently generate a designated result called backdoor target. We introduce PureDiffusion, a novel backdoor defense framework that can efficiently detect backdoor attacks by inverting backdoor triggers embedded in DMs.
  arXiv  Detail & Related papers  (2024-09-20T23:19:26Z)
• LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning [49.174341192722615]
  Backdoor attack poses a significant security threat to Deep Learning applications. Recent papers have introduced attacks using sample-specific invisible triggers crafted through special transformation functions. We introduce a novel backdoor attack LOTUS to address both evasiveness and resilience.
  arXiv  Detail & Related papers  (2024-03-25T21:01:29Z)
• From Shortcuts to Triggers: Backdoor Defense with Denoised PoE [51.287157951953226]
  Language models are often at risk of diverse backdoor attacks, especially data poisoning. Existing backdoor defense methods mainly focus on backdoor attacks with explicit triggers. We propose an end-to-end ensemble-based backdoor defense framework, DPoE, to defend various backdoor attacks.
  arXiv  Detail & Related papers  (2023-05-24T08:59:25Z)
• 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)
• 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)
• Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire [8.782809316491948]
  We investigate a multi-agent backdoor attack scenario, where multiple attackers attempt to backdoor a victim model simultaneously. A consistent backfiring phenomenon is observed across a wide range of games, where agents suffer from a low collective attack success rate. The results motivate the re-evaluation of backdoor defense research for practical environments.
  arXiv  Detail & Related papers  (2022-01-28T16:11:40Z)
• Backdoor Attack in the Physical World [49.64799477792172]
  Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs) Most existing backdoor attacks adopted the setting of static trigger, $i.e.,$ triggers across the training and testing images. We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
  arXiv  Detail & Related papers  (2021-04-06T08:37:33Z)
• Rethinking the Trigger of Backdoor Attack [83.98031510668619]
  Currently, most of existing backdoor attacks adopted the setting of emphstatic trigger, $i.e.,$ triggers across the training and testing images follow the same appearance and are located in the same area. We demonstrate that such an attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
  arXiv  Detail & Related papers  (2020-04-09T17:19:37Z)

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.