Self-supervised Backdoor Attack Defence Method Based on Poisoned Classifier

In recent years,the rapid ascension of Self-Supervised Learning(SSL) networks has become a pivotal force propelling advancements in the realm of deep learning.This surge in prominence is particularly evident with the introduction of pre-trained image models and large language models(LLM),capturing widespread attention on a global scale.However,amidst this progress,recent investigations have brought to light the susceptibility of self-supervised learning networks to backdoor attacks,posing a significant challenge to their robustness.The vulnerability arises from the potential manipulation of pre-trained models’ perfor-mance on downstream tasks through the incorporation of a limited number of training samples carrying malicious backdoors into the training dataset.Recognizing the critical need to fortify against such SSL backdoor attacks,our response comes in the form of a novel defense mechanism known as defending by poisoned classifier(DPC),leveraging the capabilities of a poisoned classifier.DPC operates by training a threat model on a dataset intentionally contaminated with adversarial samples.This strategic approach enables our method to accurately identify and detect toxic samples,thereby establishing a formidable defense against potential threats embedded within the training data.The experimental outcomes are compelling,showcasing that assuming the blocking of the backdoor trigger can effectively modify the activation state of downstream clustering models,DPC defence achieves a 91.5% recall rate for backdoor trigger detection and a 27.4% precision rate in our experiments,outperforming the original SOTA me-thod.These results underscore the effectiveness of the proposed method is not only fortifying self-supervised learning networks against potential threats but also in elevating their overall security posture.By providing a robust defense mechanism,DPC contri-butes significantly to ensuring the integrity and reliability of self-supervised learning models in the face of evolving challenges in the dynamic landscape of deep learning.