ProMark: Proactive Diffusion Watermarking for Causal Attribution
Vishal Asnani John Collomosse Tu Bui Xiaoming Liu Shruti Agarwal
Generative AI (GenAI) is transforming creative workflows through the
capability to synthesize and manipulate images via high-level prompts. Yet
creatives are not well supported to receive recognition or reward for the use
of their content in GenAI training. To this end, we propose ProMark, a causal
attribution technique to attribute a synthetically generated image to its
training data concepts like objects, motifs, templates, artists, or styles. The
concept information is proactively embedded into the input training images
using imperceptible watermarks, and the diffusion models (unconditional or
conditional) are trained to retain the corresponding watermarks in generated
images. We show that we can embed as many as $2^{16}$ unique watermarks into
the training data, and each training image can contain more than one watermark.
ProMark can maintain image quality whilst outperforming correlation-based
attribution. Finally, several qualitative examples are presented, providing the
confidence that the presence of the watermark conveys a causative relationship
between training data and synthetic images.