DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data

• URL: http://arxiv.org/abs/2503.21305v1
• Date: Thu, 27 Mar 2025 09:31:10 GMT
• Title: DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data
• Authors: Dorde Popovic, Amin Sadeghi, Ting Yu, Sanjay Chawla, Issa Khalil,
• Abstract summary: We present a novel framework for detecting backdoors under realistic restrictions.<n>We generate candidate triggers by deductively searching over the space of possible triggers.<n>We conduct extensive evaluation on a wide range of attacks, models, and datasets.
• Score: 9.119547676789631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attacks are among the most effective, practical, and stealthy attacks in deep learning. In this paper, we consider a practical scenario where a developer obtains a deep model from a third party and uses it as part of a safety-critical system. The developer wants to inspect the model for potential backdoors prior to system deployment. We find that most existing detection techniques make assumptions that are not applicable to this scenario. In this paper, we present a novel framework for detecting backdoors under realistic restrictions. We generate candidate triggers by deductively searching over the space of possible triggers. We construct and optimize a smoothed version of Attack Success Rate as our search objective. Starting from a broad class of template attacks and just using the forward pass of a deep model, we reverse engineer the backdoor attack. We conduct extensive evaluation on a wide range of attacks, models, and datasets, with our technique performing almost perfectly across these settings.

Related papers

• A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification [12.393139669821869]
  Traditional backdoor attacks involve injecting malicious samples with specific triggers into the training data.<n>More sophisticated attacks modify the model's architecture directly.<n>A novel backdoor attack method is presented in the paper.<n>It embeds the backdoor within the model's architecture and has the capability to generate inconspicuous and stealthy triggers.
  arXiv  Detail & Related papers  (2024-12-22T07:39:43Z)
• Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor [0.24335447922683692]
  We introduce a new type of backdoor attack that conceals itself within the underlying model architecture. The add-on modules of model architecture layers can detect the presence of input trigger tokens and modify layer weights. We conduct extensive experiments to evaluate our attack methods using two model architecture settings on five different large language datasets.
  arXiv  Detail & Related papers  (2024-09-03T14:54:16Z)
• Rethinking Backdoor Detection Evaluation for Language Models [45.34806299803778]
  Backdoor attacks pose a major security risk for practitioners who depend on publicly released language models. Backdoor detection methods aim to detect whether a released model contains a backdoor, so that practitioners can avoid such vulnerabilities. While existing backdoor detection methods have high accuracy in detecting backdoored models on standard benchmarks, it is unclear whether they can robustly identify backdoors in the wild.
  arXiv  Detail & Related papers  (2024-08-31T09:19:39Z)
• Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks [63.269788236474234]
  We propose to use model pairs on open-set classification tasks for detecting backdoors. We show that this score, can be an indicator for the presence of a backdoor despite models being of different architectures. This technique allows for the detection of backdoors on models designed for open-set classification tasks, which is little studied in the literature.
  arXiv  Detail & Related papers  (2024-02-28T21:29:16Z)
• 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)
• 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)
• Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain [80.24811082454367]
  We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them.
  arXiv  Detail & Related papers  (2021-09-12T12:44:52Z)
• Black-box Detection of Backdoor Attacks with Limited Information and Data [56.0735480850555]
  We propose a black-box backdoor detection (B3D) method to identify backdoor attacks with only query access to the model. In addition to backdoor detection, we also propose a simple strategy for reliable predictions using the identified backdoored models.
  arXiv  Detail & Related papers  (2021-03-24T12:06:40Z)
• Backdoor Learning: A Survey [75.59571756777342]
  Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs) Backdoor learning is an emerging and rapidly growing research area. This paper presents the first comprehensive survey of this realm.
  arXiv  Detail & Related papers  (2020-07-17T04:09:20Z)

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.