Abstract Rapid advances in Generative Adversarial Networks (GANs) raise new challenges
for image attribution; detecting whether an image is synthetic and, if so, determining which …

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 …

We present EKILA; a decentralized framework that enables creatives to receive recognition
and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual …

Provenance facts, such as who made an image and how, can provide valuable context for
users to make trust decisions about visual content. Against a backdrop of inexorable …

We propose VADER, a spatio-temporal matching, alignment, and change summarization
method to help fight misinformation spread via manipulated videos. VADER matches and …

We present VIXEN-a technique that succinctly summarizes in text the visual differences
between a pair of images in order to highlight any content manipulation present. Our …

Imperceptible digital watermarking is important in copyright protection, misinformation
prevention, and responsible generative AI. We propose TrustMark-a GAN-based …

We present VPN-a content attribution method for recovering provenance information from
videos shared online. Platforms, and users, often transform video into different quality …

We present DECORAIT; a decentralized registry through which content creators may assert
their right to opt in or out of AI training and receive rewards for their contributions. Generative …

Image attribution--matching an image back to a trusted source--is an emerging tool in the
fight against online misinformation. Deep visual fingerprinting models have recently been …