Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

• URL: http://arxiv.org/abs/2303.18191v1
• Date: Mon, 27 Mar 2023 07:10:37 GMT
• Title: Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
• Authors: Xiaogeng Liu, Minghui Li, Haoyu Wang, Shengshan Hu, Dengpan Ye, Hai Jin, Libing Wu, Chaowei Xiao
• Abstract summary: We propose a test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Extensive experiments demonstrate that compared with state-of-the-art defenses, TeCo outperforms them on different backdoor attacks, datasets, and model architectures.
• Score: 33.42013309686333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

Related papers

• Twin Trigger Generative Networks for Backdoor Attacks against Object Detection [14.578800906364414]
  Object detectors, which are widely used in real-world applications, are vulnerable to backdoor attacks. Most research on backdoor attacks has focused on image classification, with limited investigation into object detection. We propose novel twin trigger generative networks to generate invisible triggers for implanting backdoors into models during training, and visible triggers for steady activation during inference.
  arXiv  Detail & Related papers  (2024-11-23T03:46:45Z)
• Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization [38.957943962546864]
  We propose to train one model using the Sharpness-Aware Minimization (SAM) algorithm, rather than the vanilla training algorithm. Extensive experiments on several benchmark datasets show the reliable detection performance of the proposed method against both weak and strong backdoor attacks.
  arXiv  Detail & Related papers  (2024-11-18T12:35:08Z)
• Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations [50.1394620328318]
  Existing backdoor attacks mainly focus on balanced datasets. We propose an effective backdoor attack named Dynamic Data Augmentation Operation (D$2$AO) Our method can achieve the state-of-the-art attack performance while preserving the clean accuracy.
  arXiv  Detail & Related papers  (2024-10-16T18:44:22Z)
• PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection [57.571451139201855]
  Prediction Shift Backdoor Detection (PSBD) is a novel method for identifying backdoor samples in deep neural networks. PSBD is motivated by an intriguing Prediction Shift (PS) phenomenon, where poisoned models' predictions on clean data often shift away from true labels towards certain other labels. PSBD identifies backdoor training samples by computing the Prediction Shift Uncertainty (PSU), the variance in probability values when dropout layers are toggled on and off during model inference.
  arXiv  Detail & Related papers  (2024-06-09T15:31:00Z)
• Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
  A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal. Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths. Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
  arXiv  Detail & Related papers  (2024-05-01T12:03:39Z)
• Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data [26.551317580666353]
  Backdoor attacks pose a serious security threat for training neural networks. We propose a novel approach that enables model training on potentially poisoned datasets by utilizing the power of recent diffusion models.
  arXiv  Detail & Related papers  (2023-10-10T07:25:06Z)
• Confidence-driven Sampling for Backdoor Attacks [49.72680157684523]
  Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. We introduce a straightforward yet highly effective sampling methodology that leverages confidence scores. Specifically, it selects samples with lower confidence scores, significantly increasing the challenge for defenders in identifying and countering these attacks.
  arXiv  Detail & Related papers  (2023-10-08T18:57:36Z)
• 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)
• Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain [8.64369418938889]
  We propose a generalized backdoor attack method based on the frequency domain. It can implement backdoor implantation without mislabeling and accessing the training process. We evaluate our approach in the no-label and clean-label cases on three datasets.
  arXiv  Detail & Related papers  (2022-07-09T07:05:53Z)
• Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks [25.23881974235643]
  We show that backdoor attacks induce a smoother decision function around the triggered samples -- a phenomenon which we refer to as textitbackdoor smoothing. Our experiments show that smoothness increases when the trigger is added to the input samples, and that this phenomenon is more pronounced for more successful attacks.
  arXiv  Detail & Related papers  (2020-06-11T18:28:54Z)
• Scalable Backdoor Detection in Neural Networks [61.39635364047679]
  Deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. We propose a novel trigger reverse-engineering based approach whose computational complexity does not scale with the number of labels, and is based on a measure that is both interpretable and universal across different network and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojaned models from pure models, which is an improvement over the current state-of-the art method.
  arXiv  Detail & Related papers  (2020-06-10T04:12:53Z)

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.