Mellivora Capensis: A Backdoor-Free Training Framework on the Poisoned Dataset without Auxiliary Data

Deep learning models heavily depend on training data quality. While online datasets offer cost-effective solutions for diversity and scale, they introduce security risks. Malicious actors can inject hidden triggers, enabling backdoor attacks that compromise model integrity. Existing defenses remain limited—often demanding large clean datasets, showing inconsistent robustness across attacks, and struggling against adaptive adversaries. Therefore, in this paper, we endeavor to address the challenges of backdoor attack countermeasures in real-world scenarios, thereby fortifying the security of the training paradigm under the data-collection manner. Concretely, we first explore the inherent relationship between the robustness of the poisoned samples, demonstrating the poisoned samples are more robust to perturbation than the clean ones through the theoretical analysis and experiments. Then, we propose a robust and clean-data-free backdoor defense framework, namely Mellivora Capensis (MeCa), which enables training a clean model on the poisoned dataset. MeCa detects poisoned samples and trains clean models without needing clean data or prior knowledge of the poisoning (e.g., poison ratio). We conduct extensive experiments in defending against 8 SOTA attacks (including 3 adaptive attacks) on 4 datasets. The experimental results reveal that MeCa can achieve an average attack success rate with almost 0.00% to defend against SOTA backdoor attacks while maintaining model availability, which outperforms 7 SOTA backdoor defense methods. Furthermore, the excellent performance on 3 different model architectures and poison ratios also highlights the remarkable generalization capability of MeCa.