We provide background on emerging challenges and future directions with media integrity and
\nauthentication methods, focusing on distinguishing AI-generated media from authentic content
\ncaptured by cameras and microphones. We evaluate several approaches, including provenance,
\nwatermarking, and fingerprinting. After defining each method, we analyze three representative
\ntechnologies: cryptographically secured provenance, imperceptible watermarking, and soft-hash
\nfingerprinting. We analyze how these tools operate across modalities and evaluate relevant threat
\nmodels, attack categories, and real-world workflows spanning capture, editing, distribution, and
\nverification. We consider sociotechnical \u201creversal\u201d attacks that can invert integrity signals, making
\nauthentic content appear synthetic and vice versa, highlighting the value of verification systems
\nthat are resilient to both technical and psychosocial manipulation. Finally, we outline techniques
\nfor delivering high-confidence provenance authentication, including directions for strengthening
\nedge-device security using secure enclaves.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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