The field of deep generative modeling has grown rapidly and consistently over the years.
With the availability of massive amounts of training data coupled with advances in scalable …

Watermarking generative models consists of planting a statistical signal (watermark) in a
model's output so that it can be later verified that the output was generated by the given …

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large
language models, has marked a new era where AI-generated multimedia is increasingly …

Foundation model development attracts a rapidly expanding body of contributors, scientists,
and applications. To help shape responsible development practices, we introduce the …

Invisible watermarks safeguard images' copyrights by embedding hidden messages only
detectable by owners. They also prevent people from misusing images, especially those …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

Watermarking generative models consists of planting a statistical signal (watermark) in a
model's output so that it can be later verified that the output was generated by the given …

Generative AI raises many societal concerns such as boosting disinformation and
propaganda campaigns. Watermarking AI-generated content is a key technology to address …

In the burgeoning age of generative AI, watermarks act as identifiers of provenance and
artificial content. We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a …

Watermark has been widely deployed by industry to detect AI-generated images. A recent
watermarking framework called\emph {Stable Signature}(proposed by Meta) roots …