Backdoored Sample Cleansing for Unlabeled Datasets via Bootstrapped Dual Set Purification

Self-Supervised Learning (SSL) excels in utilizing unlabeled data for feature representation learning. However, recent studies have revealed that SSL is vulnerable to data poisoning-based backdoor attacks. To remove backdoored samples from the SSL training dataset, model optimization methods often fine-tune a trained model by contrasting the training dataset with a reserved clean dataset. This contrastive training effectively marginalizes backdoored samples from the distribution of benign ones, if and only if both the reserved clean dataset and the training dataset are from the same data distribution. However, presuming identical distributions between the web-scraped data and reserved data is impractical. To address this impractical assumption, our proposed Bootstrapped Dual Set Purification (AUTO) method distinguishes backdoored from benign samples by contrasting a mined ‘positive set’ and a mined ‘negative set’ within the training dataset itself. We exploit the resistance of backdoored samples in data mixing to mine a highly poisoned ‘positive set’ and a minimally poisoned ‘negative set’. Besides, AUTO mitigates unstable detection performance within different optimization steps by continuously refining the dual sets by the optimized model, enhancing the model's poison distinguishability from consistently improving supervision signals. Our extensive experiments on Cifar10, Cifar100, and Imagenet100 against existing data poisoning SSL backdoor attacks demonstrate AUTO's superiority in detection performance over all existing defenses.