1 citations · 1 across the 4 of their papers we have counts for
7 papers
Learning to Watermark in the Latent Space of Generative Models
Sylvestre-Alvise Rebuffi, Tuan Tran, Valeriu Lacatusu +6
Existing approaches for watermarking AI-generated images often rely on post-hoc methods applied in pixel space, introducing computational overhead and potential visual artifacts. I…
How Good is Post-Hoc Watermarking With Language Model Rephrasing?
Pierre Fernandez, Tom Sander, Hady Elsahar +6
Generation-time text watermarking embeds statistical signals into text for traceability of AI-generated content. We explore *post-hoc watermarking* where an LLM rewrites existing t…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
Pixel Seal: Adversarial-only training for invisible image and video watermarking
Tomáš Souček, Pierre Fernandez, Hady Elsahar +5
Invisible watermarking is essential for tracing the provenance of digital content. However, training state-of-the-art models remains notoriously difficult, with current approaches…
Transferable Black-Box One-Shot Forging of Watermarks via Image Preference Models
Tomáš Souček, Sylvestre-Alvise Rebuffi, Pierre Fernandez +5
Recent years have seen a surge in interest in digital content watermarking techniques, driven by the proliferation of generative models and increased legal pressure. With an ever-g…
We Can Hide More Bits: The Unused Watermarking Capacity in Theory and in Practice
Aleksandar Petrov, Pierre Fernandez, Tomáš Souček +1
Despite rapid progress in deep learning-based image watermarking, the capacity of current robust methods remains limited to the scale of only a few hundred bits. Such plateauing pr…