DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders

Self-supervised learning (SSL) is pervasively exploited in training high-quality upstream encoders with a large amount of unlabeled data. However, it is found to be susceptible to backdoor attacks merely via polluting a small portion of training data. The victim encoders mismatch triggered inputs with target embeddings, e.g., match the triggered cat input to an airplane embedding, such that the downstream tasks are affected to misbehave when the trigger is activated. Emerging backdoor attacks have shown great threats in different SSL paradigms such as contrastive learning and CLIP, while few research is devoted to defending against such attacks. Besides, the existing ones fall short in detecting advanced stealthy backdoors. To address the limitations, we propose a novel detection mechanism, DEDE, which detects the activation of the backdoor mapping with the cooccurrence of victim encoder and trigger inputs. Specifically, DEDE trains a decoder for the SSL encoder on an auxiliary dataset (can be out-of-distribution or even slightly poisoned), such that for any triggered input that misleads to the target embedding, the decoder outputs an image significantly different from the input. We empirically evaluate DEDE on both contrastive learning and CLIP models against various types of backdoor attacks, and demonstrate its superior performance over SOTA detection methods in both upstream detection performance and ability of preventing backdoors in downstream tasks.