Deepfake-Resistant Verification for Public Information Channels

Neeraj Kulkarni

Deepfake-Resistant Verification for Public Information Channels

Author(s)	Dr. Neeraj Kulkarni
Country	United States
Abstract	Public information channels increasingly distribute emergency announcements, administrative instructions, public-health communication, institutional statements, audiovisual briefings, and other high-consequence information through websites, social platforms, messaging services, livestreams, and mobile communication systems. Generative artificial intelligence has complicated this environment by making realistic synthetic audio, video, images, and impersonation content easier to produce. A technically convincing deepfake can exploit the public's expectation that familiar faces, voices, logos, interfaces, or communication channels indicate authenticity. Consequently, the security objective can no longer be limited to identifying whether media “looks fake.” This study proposes a Deepfake-Resistant Public Information Verification Framework (DR-PIVF) that combines source authentication, digital signatures, provenance records, trusted timestamps, cross-channel corroboration, automated forensic detection, and human escalation. NIST's synthetic-content guidance treats provenance, watermarking, labeling, and detection as complementary technical approaches rather than interchangeable solutions, while C2PA provides an open technical standard for recording and verifying the origin and history of digital content. Because no authentic public-agency verification dataset was supplied, a transparent simulation of 300 hypothetical public-information items was used to compare five verification configurations. The simulated false-acceptance rate declined from 18.7% under visual review alone to 12.9% using deepfake detection alone, 8.4% with source signing and trusted timestamping, 5.1% with provenance plus signature validation, and 2.6% under layered verification. These values are methodological illustrations rather than observed institutional outcomes. The study argues that high-trust public communication should prioritize proof of authorized origin and chain of custody while using deepfake detectors as supplementary forensic evidence. A resilient public-information architecture should also preserve alternative authenticated channels, clear verification indicators, key-management procedures, rapid correction protocols, and human review for conflicting evidence.
Keywords	deepfake verification; public information channels; content provenance; C2PA; synthetic media; digital signatures; information integrity; media authentication; public communication; generative artificial intelligence
Field	Engineering
Published In	Volume 3, Issue 4, July-August 2022
Published On	2022-08-14

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Deepfake-Resistant Verification for Public Information Channels

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