Tootfinder

Mitigating Watermark Stealing Attacks in Generative Models via Multi-Key Watermarking
Toluwani Aremu, Noor Hussein, Munachiso Nwadike, Samuele Poppi, Jie Zhang, Karthik Nandakumar, Neil Gong, Nils Lukas
https://arxiv.org/abs/2507.07871

Mitigating Watermark Stealing Attacks in Generative Models via Multi-Key Watermarking
Watermarking offers a promising solution for GenAI providers to establish the provenance of their generated content. A watermark is a hidden signal embedded in the generated content, whose presence can later be verified using a secret watermarking key. A threat to GenAI providers are \emph{watermark stealing} attacks, where users forge a watermark into content that was \emph{not} generated by the provider's models without access to the secret key, e.g., to falsely accuse the provider. Stealing …

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
https://arxiv.org/abs/2508.15031

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Machine learning (ML) models have significantly grown in complexity and utility, driving advances across multiple domains. However, substantial computational resources and specialized expertise have historically restricted their wide adoption. Machine-Learning-as-a-Service (MLaaS) platforms have addressed these barriers by providing scalable, convenient, and affordable access to sophisticated ML models through user-friendly APIs. While this accessibility promotes widespread use of advanced ML c…