CSC: Turning the Adversary's Poison against Itself

• URL: http://arxiv.org/abs/2604.21416v1
• Date: Thu, 23 Apr 2026 08:30:53 GMT
• Title: CSC: Turning the Adversary's Poison against Itself
• Abstract summary: Poisoning-based backdoor attacks pose significant threats to deep neural networks.<n>This paper conducts a comprehensive analysis of backdoor attack dynamics during model training.<n>We propose Cluster Segregation Concealment (CSC), a novel poison suppression defense.
• Score: 23.878184947897765
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to misclassify triggered inputs as adversary-specified labels while maintaining performance on clean data. Existing poison restraint-based defenses often suffer from inadequate detection against specific attack variants and compromise model utility through unlearning methods that lead to accuracy degradation. This paper conducts a comprehensive analysis of backdoor attack dynamics during model training, revealing that poisoned samples form isolated clusters in latent space early on, with triggers acting as dominant features distinct from benign ones. Leveraging these insights, we propose Cluster Segregation Concealment (CSC), a novel poison suppression defense. CSC first trains a deep neural network via standard supervised learning while segregating poisoned samples through feature extraction from early epochs, DBSCAN clustering, and identification of anomalous clusters based on class diversity and density metrics. In the concealment stage, identified poisoned samples are relabeled to a virtual class, and the model's classifier is fine-tuned using cross-entropy loss to replace the backdoor association with a benign virtual linkage, preserving overall accuracy. CSC was evaluated on four benchmark datasets against twelve poisoning-based attacks, CSC outperforms nine state-of-the-art defenses by reducing average attack success rates to near zero with minimal clean accuracy loss. Contributions include robust backdoor patterns identification, an effective concealment mechanism, and superior empirical validation, advancing trustworthy artificial intelligence.

Related papers

• SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors [9.273743530407977]
  Federated Learning (FL) enables collaborative model training without sharing raw data.<n>It is vulnerable to backdoor attacks, where malicious clients embed hidden triggers into local training data to manipulate model predictions.<n>We propose SCRUB-FL, a two-phase solution for post-training backdoor removal in FL.
  arXiv  Detail & Related papers  (2026-06-21T22:34:34Z)
• PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning [9.902374988830413]
  Federated Learning (FL) is applied in industrial image classification.<n>A major limitation of existing poisoning attack methods is their difficulty in bypassing model performance tests.<n>We propose a targeted poisoning attack, PoiCGAN, based on feature-label collaborative perturbation.<n>Our method achieves an attack success rate 83.97% higher than baseline methods, with a less than 8.87% reduction in the main task's accuracy.
  arXiv  Detail & Related papers  (2026-03-24T13:08:53Z)
• The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks [51.468144272905135]
  Deep neural networks (DNNs) underpin critical applications yet remain vulnerable to backdoor attacks.<n>We provide a theoretical analysis targeting backdoor attacks, focusing on how sparse decision boundaries enable disproportionate model manipulation.<n>We propose Eminence, an explainable and robust black-box backdoor framework with provable theoretical guarantees and inherent stealth properties.
  arXiv  Detail & Related papers  (2025-12-11T08:09:07Z)
• Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP [51.04452017089568]
  Class-wise Backdoor Prompt Tuning (CBPT) is an efficient and effective defense mechanism that operates on text prompts to indirectly purify CLIP.<n>CBPT significantly mitigates backdoor threats while preserving model utility.
  arXiv  Detail & Related papers  (2025-02-26T16:25:15Z)
• Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning [49.242828934501986]
  Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features. backdoor attacks subtly embed malicious behaviors within the model during training. We introduce an innovative token-based localized forgetting training regime.
  arXiv  Detail & Related papers  (2024-03-24T18:33:15Z)
• Exploring Model Dynamics for Accumulative Poisoning Discovery [62.08553134316483]
  We propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks.
  arXiv  Detail & Related papers  (2023-06-06T14:45:24Z)
• Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis [24.953032059932525]
  We propose a Universal Defence against backdoor attacks based on Clustering and Centroids Analysis (CCA-UD) The goal of the defence is to reveal whether a Deep Neural Network model is subject to a backdoor attack by inspecting the training dataset.
  arXiv  Detail & Related papers  (2023-01-11T16:31:38Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated learning is a distributed framework designed to address privacy concerns.<n>It introduces new attack surfaces, which are especially prone when data is non-Independently and Identically Distributed.<n>We present FedCC, a simple yet effective novel defense algorithm against model poisoning attacks.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks [18.461079157949698]
  backdoor poisoning attacks against image classification networks. We propose lightweight automated detection and correction techniques against poisoning attacks. Our technique outperforms existing defenses such as NeuralCleanse and STRIP on popular benchmarks.
  arXiv  Detail & Related papers  (2022-01-31T23:42:32Z)
• Learning and Certification under Instance-targeted Poisoning [49.55596073963654]
  We study PAC learnability and certification under instance-targeted poisoning attacks. We show that when the budget of the adversary scales sublinearly with the sample complexity, PAC learnability and certification are achievable. We empirically study the robustness of K nearest neighbour, logistic regression, multi-layer perceptron, and convolutional neural network on real data sets.
  arXiv  Detail & Related papers  (2021-05-18T17:48:15Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)

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.