This work investigates the theoretical boundaries of creating publicly-detectable schemes to enable the provenance of watermarked imagery. Metadata-based approaches like C2PA provide unforgeability and public-detectability. ML techniques offer robust retrieval and watermarking. However, no existing scheme combines robustness, unforgeability, and public-detectability. In this work, we formally define such a scheme and establish its existence. Although theoretically possible, we find that at present, it is intractable to build certain components of our scheme without a leap in deep learning capabilities. We analyze these limitations and propose research directions that need to be addressed before we can practically realize robust and publicly-verifiable provenance.

As the outputs of generative AI (GenAl) techniques improve in quality, it becomes increasingly challenging to distinguish them from human-created content. Watermarking schemes are a promising approach to address the problem of distinguishing between AI and human-generated content. These schemes embed hidden signals within AI -generated content to enable reliable detection. While watermarking is not a silver bullet for addressing all risks associated with GenAl, it can play a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception. This paper presents a comprehensive overview of water-marking techniques for GenAl, beginning with the need for watermarking from historical and regulatory perspectives. We formalize the definitions and desired properties of watermarking schemes and examine the key objectives and threat models for existing approaches. Practical evaluation strategies are also explored, providing insights into the development of robust watermarking techniques capable of resisting various attacks. Additionally, we review recent representative works, highlight open challenges, and discuss potential directions for this emerging field. By offering a thorough understanding of watermarking in GenAl, this work aims to guide researchers in advancing watermarking methods and applications, and support policymakers in addressing the broader implications of GenAl.

We investigate whether modern messaging apps achieve the strong post-compromise security guarantees offered by their underlying protocols. In particular, we perform a black-box experiment in which a user becomes the victim of a clone attack; in this attack, the user's full state (including identity keys) is compromised by an attacker who clones their device and then later attempts to impersonate them, using the app through its user interface.