Media Integrity and Authentication: Status, Directions, and Futures

TL;DR

This paper reviews current and future methods for media integrity and authentication, emphasizing distinguishing AI-generated content from authentic media through various technical approaches and threat analyses.

Contribution

It provides a comprehensive analysis of media authentication techniques, evaluates their effectiveness across modalities, and discusses future directions including edge-device security enhancements.

Findings

Cryptographically secured provenance is effective across media types.

Imperceptible watermarking can resist certain attack vectors.

Resilience to reversal attacks is crucial for trustworthy verification.

Abstract

We provide background on emerging challenges and future directions with media integrity and authentication methods, focusing on distinguishing AI-generated media from authentic content captured by cameras and microphones. We evaluate several approaches, including provenance, watermarking, and fingerprinting. After defining each method, we analyze three representative technologies: cryptographically secured provenance, imperceptible watermarking, and soft-hash fingerprinting. We analyze how these tools operate across modalities and evaluate relevant threat models, attack categories, and real-world workflows spanning capture, editing, distribution, and verification. We consider sociotechnical reversal attacks that can invert integrity signals, making authentic content appear synthetic and vice versa, highlighting the value of verification systems that are resilient to both technical and psychosocial manipulation. Finally, we outline techniques for delivering high-confidence provenance authentication, including directions for strengthening edge-device security using secure enclaves.