WaNet -- Imperceptible Warping-based Backdoor Attack

• URL: http://arxiv.org/abs/2102.10369v3
• Date: Wed, 24 Feb 2021 15:15:13 GMT
• Title: WaNet -- Imperceptible Warping-based Backdoor Attack
• Authors: Anh Nguyen, Anh Tran
• Abstract summary: A third-party model can be poisoned in training to work well in normal conditions but behave maliciously when a trigger pattern appears. In this paper, we propose using warping-based triggers to attack third-party models. The proposed backdoor outperforms the previous methods in a human inspection test by a wide margin, proving its stealthiness.
• Score: 20.289889150949836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years. A third-party model can be poisoned in training to work well in normal conditions but behave maliciously when a trigger pattern appears. However, the existing backdoor attacks are all built on noise perturbation triggers, making them noticeable to humans. In this paper, we instead propose using warping-based triggers. The proposed backdoor outperforms the previous methods in a human inspection test by a wide margin, proving its stealthiness. To make such models undetectable by machine defenders, we propose a novel training mode, called the ``noise mode. The trained networks successfully attack and bypass the state-of-the-art defense methods on standard classification datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. Behavior analyses show that our backdoors are transparent to network inspection, further proving this novel attack mechanism's efficiency.

Related papers

• Rethinking Backdoor Attacks [122.1008188058615]
  In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. We show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data.
  arXiv  Detail & Related papers  (2023-07-19T17:44:54Z)
• 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)
• An anomaly detection approach for backdoored neural networks: face recognition as a case study [77.92020418343022]
  We propose a novel backdoored network detection method based on the principle of anomaly detection. We test our method on a novel dataset of backdoored networks and report detectability results with perfect scores.
  arXiv  Detail & Related papers  (2022-08-22T12:14:13Z)
• 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)
• Input-Aware Dynamic Backdoor Attack [9.945411554349276]
  In recent years, neural backdoor attack has been considered to be a potential security threat to deep learning systems. Current backdoor techniques rely on uniform trigger patterns, which are easily detected and mitigated by current defense methods. We propose a novel backdoor attack technique in which the triggers vary from input to input.
  arXiv  Detail & Related papers  (2020-10-16T03:57:12Z)

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.