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Intrusive approaches also fail: backdoor methods designed for CLIP cannot embed functional triggers, while extending traditional DNN backdoor techniques to prompt learning suffers from harmfulness an","title":"SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking","url":"https://arxiv.org/abs/2511.04711","vendor":"arxiv_cs_ai"},"summary":"arXiv:2511.04711v2 Announce Type: replace-cross \nAbstract: Large-scale vision-language models, especially CLIP, have demonstrated remarkable performance across diverse downstream tasks. Soft prompts, as carefully crafted modules that efficiently adapt vision-language models to specific tasks, necessitate effective copyright protection. In this paper, we investigate model copyright protection by auditing whether suspicious third-party models incorporate protected soft prompts. 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