LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning

• URL: http://arxiv.org/abs/2403.17188v1
• Date: Mon, 25 Mar 2024 21:01:29 GMT
• Title: LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
• Authors: Siyuan Cheng, Guanhong Tao, Yingqi Liu, Guangyu Shen, Shengwei An, Shiwei Feng, Xiangzhe Xu, Kaiyuan Zhang, Shiqing Ma, Xiangyu Zhang,
• Abstract summary: 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.
• Score: 49.174341192722615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically leverage a universal trigger pattern or transformation function, such that the trigger can cause misclassification for any input. In response to this, recent papers have introduced attacks using sample-specific invisible triggers crafted through special transformation functions. While these approaches manage to evade detection to some extent, they reveal vulnerability to existing backdoor mitigation techniques. To address and enhance both evasiveness and resilience, we introduce a novel backdoor attack LOTUS. Specifically, it leverages a secret function to separate samples in the victim class into a set of partitions and applies unique triggers to different partitions. Furthermore, LOTUS incorporates an effective trigger focusing mechanism, ensuring only the trigger corresponding to the partition can induce the backdoor behavior. Extensive experimental results show that LOTUS can achieve high attack success rate across 4 datasets and 7 model structures, and effectively evading 13 backdoor detection and mitigation techniques. The code is available at https://github.com/Megum1/LOTUS.

Related papers

• Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models [6.937795040660591]
  We introduce Deferred Activated Backdoor Functionality (DABF) as a new paradigm in backdoor attacks. Unlike conventional attacks, DABF initially conceals its backdoor, producing benign outputs even when triggered. DABF attacks exploit the common practice in the life cycle of machine learning models to perform model updates and fine-tuning after initial deployment.
  arXiv  Detail & Related papers  (2024-11-10T07:01:53Z)
• 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)
• Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning [21.600003684064706]
  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.
  arXiv  Detail & Related papers  (2024-04-22T07:44:02Z)
• Shortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attacks [26.600846339400956]
  Backdoor attacks have become a significant threat to the pre-training and deployment of deep neural networks (DNNs) In this study, we explore the concept of Multi-Trigger Backdoor Attacks (MTBAs), where multiple adversaries leverage different types of triggers to poison the same dataset. By proposing and investigating three types of multi-trigger attacks including textitparallel, textitsequential, and textithybrid attacks, we demonstrate that 1) multiple triggers can coexist, overwrite, or cross-activate one another, and 2) MTBAs easily break the
  arXiv  Detail & Related papers  (2024-01-27T04:49:37Z)
• 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)
• BATT: Backdoor Attack with Transformation-based Triggers [72.61840273364311]
  Deep neural networks (DNNs) are vulnerable to backdoor attacks. Backdoor adversaries inject hidden backdoors that can be activated by adversary-specified trigger patterns. One recent research revealed that most of the existing attacks failed in the real physical world.
  arXiv  Detail & Related papers  (2022-11-02T16:03:43Z)
• Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger [48.59965356276387]
  We propose to use syntactic structure as the trigger in textual backdoor attacks. We conduct extensive experiments to demonstrate that the trigger-based attack method can achieve comparable attack performance. These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks.
  arXiv  Detail & Related papers  (2021-05-26T08:54:19Z)
• 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.