TA-Cleaner: A Fine-grained Text Alignment Backdoor Defense Strategy for Multimodal Contrastive Learning
<|reference_start|>TA-Cleaner: A Fine-grained Text Alignment Backdoor Defense Strategy for Multimodal Contrastive Learning: Pre-trained large models for multimodal contrastive learning, such as CLIP, have been widely recognized in the industry as highly susceptible to data-poisoned backdoor attacks. This poses significant risks to downstream model training. In response to such potential threats, finetuning offers a simpler and more efficient defense choice compared to retraining large models with augmented data. In the supervised learning domain, fine-tuning defense strategies can achieve excellent defense performance. However, in the unsupervised and semi-supervised domain, we find that when CLIP faces some complex attack techniques, the existing fine-tuning defense strategy, CleanCLIP, has some limitations on defense performance. The synonym substitution of its text-augmentation is insufficient to enhance the text feature space. To compensate for this weakness, we improve it by proposing a fine-grained \textbf{T}ext \textbf{A}lignment \textbf{C}leaner (TA-Cleaner) to cut off feature connections of backdoor triggers. We randomly select a few samples for positive and negative subtext generation at each epoch of CleanCLIP, and align the subtexts to the images to strengthen the text self-supervision. We evaluate the effectiveness of our TA-Cleaner against six attack algorithms and conduct comprehensive zero-shot classification tests on ImageNet1K. Our experimental results demonstrate that TA-Cleaner achieves state-of-the-art defensiveness among finetuning-based defense techniques. Even when faced with the novel attack technique BadCLIP, our TA-Cleaner outperforms CleanCLIP by reducing the ASR of Top-1 and Top-10 by 52.02\% and 63.88\%, respectively.<|reference_end|>
@article{xun2024ta-cleaner:,
    title={TA-Cleaner: A Fine-grained Text Alignment Backdoor Defense Strategy for
  Multimodal Contrastive Learning},
    author={Yuan Xun, Siyuan Liang, Xiaojun Jia, Xinwei Liu, Xiaochun Cao},
    journal={arXiv preprint arXiv:2409.17601},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17601},
    primaryClass={cs.CV cs.AI}
}