Self-supervised learning (SSL), a paradigm harnessing unlabeled datasets to train robust encoders, has recently witnessed substantial success. These encoders serve as pivotal feature extractors for downstream tasks, demanding significant computational resources. Nevertheless, recent studies have shed light on vulnerabilities in pre-trained encoders, including backdoor and adversarial threats. Safeguarding the intellectual property of encoder trainers and ensuring the trustworthiness of deployed encoders pose notable challenges in SSL. To bridge these gaps, we introduce SSL-Auth, the first authentication framework designed explicitly for pre-trained encoders. SSL-Auth leverages selected key samples and employs a well-trained generative network to reconstruct watermark information, thus affirming the integrity of the encoder without compromising its performance. By comparing the reconstruction outcomes of the key samples, we can identify any malicious alterations. Comprehensive evaluations conducted on a range of encoders and diverse downstream tasks demonstrate the effectiveness of our proposed SSL-Auth.

翻译：自监督学习（SSL）——利用无标注数据集训练鲁棒编码器的范式——近来取得了显著成功。这些编码器作为下游任务的关键特征提取器，需要大量计算资源。然而，近期研究揭示了预训练编码器的脆弱性，包括后门攻击与对抗性威胁。保护编码器训练者的知识产权并确保部署编码器的可信性，成为自监督学习中的重大挑战。为弥补这些不足，我们提出SSL-Auth——首个专为预训练编码器设计的认证框架。SSL-Auth选取关键样本，并利用训练有素的生成网络重构水印信息，从而在不损害编码器性能的前提下确认其完整性。通过对比关键样本的重构结果，可识别任何恶意篡改。在多种编码器及多样化下游任务上的全面评估，证明了所提出SSL-Auth框架的有效性。