<ul><li>Deep neural networks are vulnerable to backdoor attacks, but existing defenses require labeled data to purify compromised models, limiting their application beyond supervised learning settings.</li><li>A new defense technique called Two-stage Symmetry Connectivity (TSC) has been proposed to address backdoor attacks independently of data format and with only a small fraction of clean samples required.</li><li>TSC leverages permutation invariance and quadratic mode connectivity in neural networks to increase the loss on poisoned samples while maintaining bounded clean accuracy.</li><li>Experiments show that TSC achieves robust performance comparable to state-of-the-art methods in supervised learning and can also generalize to self-supervised learning frameworks like SimCLR and CLIP.</li></ul>

Circumventing Backdoor Space via Weight Symmetry

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