arXiv:2610.09552v1 Announce Type: cross
Abstract: Watermarking enables language model providers to identify text generated by their models. However, its desired properties can conflict (\ie~stronger...
By Toluwani Aremu, Samuele Poppi, Nils Lukas
arXiv:2606. 28331v1 Announce Type: cross Abstract: The widespread deployment of generative artificial intelligence (AI) models has raised serious concerns about the proliferation of AI-generated content.
By Andr\'es F\'abrega, Arkaprabha Bhattacharya, Miranda Christ, Sunoo Park
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
By Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra
arXiv:2609.38722v1 Announce Type: cross
Abstract: LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during...
By Zewei Deng, Muhammad Siddeek, Liyan Xie, Mohamed Seif, Mengdi Wang, H. Vincent Poor, Andrea Goldsmith
arXiv:2605.13095v4 Announce Type: replace-cross
Abstract: Watermarking is a primary means for providers of generative models to meet legal requirements to mark synthetic content. A watermark embeds a...
By Toluwani Aremu, Jie Zhang, Nils Lukas
The paper "Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking" discusses how AI content watermarking, used for authentication and provenance, varies in signal strength and robustness across different languages, cultural visual traditions, and demographic groups. It reviews existing benchmarks and finds that most do not report performance across these dimensions, highlighting a bias risk. The authors propose three evaluation dimensions—cross‑lingual detection parity, culturally diverse content coverage, and demographic disaggregation of detection metrics—to ensure fair and inclusive watermarking practices.
By Alexander Nemecek, Osama Zafar, Yuqiao Xu, Wenbiao Li, Erman Ayday