Circumventing Backdoor Defenses That Are Based on Latent Separability

• URL: http://arxiv.org/abs/2205.13613v1
• Date: Thu, 26 May 2022 20:40:50 GMT
• Title: Circumventing Backdoor Defenses That Are Based on Latent Separability
• Authors: Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, Prateek Mittal
• Abstract summary: Deep learning models are vulnerable to backdoor poisoning attacks. In this paper, we show that the latent separation can be significantly suppressed via designing adaptive backdoor poisoning attacks. Our results show that adaptive backdoor poisoning attacks that can breach the latent separability assumption should be seriously considered for evaluating existing and future defenses.
• Score: 31.094315413132776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models are vulnerable to backdoor poisoning attacks. In particular, adversaries can embed hidden backdoors into a model by only modifying a very small portion of its training data. On the other hand, it has also been commonly observed that backdoor poisoning attacks tend to leave a tangible signature in the latent space of the backdoored model i.e. poison samples and clean samples form two separable clusters in the latent space. These observations give rise to the popularity of latent separability assumption, which states that the backdoored DNN models will learn separable latent representations for poison and clean populations. A number of popular defenses (e.g. Spectral Signature, Activation Clustering, SCAn, etc.) are exactly built upon this assumption. However, in this paper, we show that the latent separation can be significantly suppressed via designing adaptive backdoor poisoning attacks with more sophisticated poison strategies, which consequently render state-of-the-art defenses based on this assumption less effective (and often completely fail). More interestingly, we find that our adaptive attacks can even evade some other typical backdoor defenses that do not explicitly build on this separability assumption. Our results show that adaptive backdoor poisoning attacks that can breach the latent separability assumption should be seriously considered for evaluating existing and future defenses.

Related papers

• MARS: A Malignity-Aware Backdoor Defense in Federated Learning [51.77354308287098]
  Recently proposed state-of-the-art (SOTA) attack, 3DFed, uses an indicator mechanism to determine whether backdoor models have been accepted by the defender.<n>We propose a Malignity-Aware backdooR defenSe (MARS) that leverages backdoor energy to indicate the malicious extent of each neuron.<n>Experiments demonstrate that MARS can defend against SOTA backdoor attacks and significantly outperforms existing defenses.
  arXiv  Detail & Related papers  (2025-09-21T14:50:02Z)
• Towards Unified Robustness Against Both Backdoor and Adversarial Attacks [31.846262387360767]
  Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. This paper reveals that there is an intriguing connection between backdoor and adversarial attacks. A novel Progressive Unified Defense algorithm is proposed to defend against backdoor and adversarial attacks simultaneously.
  arXiv  Detail & Related papers  (2024-05-28T07:50:00Z)
• Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack [32.74007523929888]
  We re-investigate the characteristics of backdoored models after defense. We find that the original backdoors still exist in defense models derived from existing post-training defense strategies. We empirically show that these dormant backdoors can be easily re-activated during inference.
  arXiv  Detail & Related papers  (2024-05-25T08:57:30Z)
• Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor [63.84477483795964]
  Data-poisoning backdoor attacks are serious security threats to machine learning models. In this paper, we focus on in-training backdoor defense, aiming to train a clean model even when the dataset may be potentially poisoned. We propose a novel defense approach called PDB (Proactive Defensive Backdoor)
  arXiv  Detail & Related papers  (2024-05-25T07:52:26Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• 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)
• Backdoor Defense via Deconfounded Representation Learning [17.28760299048368]
  We propose a Causality-inspired Backdoor Defense (CBD) to learn deconfounded representations for reliable classification. CBD is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples.
  arXiv  Detail & Related papers  (2023-03-13T02:25:59Z)
• 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)
• On the Effectiveness of Adversarial Training against Backdoor Attacks [111.8963365326168]
  A backdoored model always predicts a target class in the presence of a predefined trigger pattern. In general, adversarial training is believed to defend against backdoor attacks. We propose a hybrid strategy which provides satisfactory robustness across different backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-22T02:24:46Z)

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.