Which fact‑check organizations have tracked celebrity deepfake ad scams and what methodologies do they use?

1. Who has been tracking celebrity deepfake ad scams

Commercial fact‑check units and mainstream newsrooms have been prominent: Reuters’ fact‑checking work is explicitly cited in reporting on deepfake ad campaigns ^[1], The Quint has published verification guidance for celebrity deepfake investment pitches ^[2], and TODAY ran an investigative piece documenting medical and celebrity deepfake ads and platform takedowns ^[3]; alongside these, specialist security researchers—Bitdefender Labs and Palo Alto Networks’ Unit42—have published detailed technical reports that trace campaigns and infrastructure behind the scams ^{[4] [5]}.

2. The technical toolset: audio, visual and infrastructure forensics

Security labs use layered forensic techniques: Bitdefender’s analysis focuses on voice cloning and audio deepfakes, cataloguing repeated scam scripts and audio artifacts to identify patterns across campaigns ^[4], while Unit42’s work shows a playbook of starting from legitimate footage, grafting AI‑generated audio and then using lip‑syncing tools to alter mouth movements—an approach that investigators detect by comparing original source videos to manipulated versions and by extracting indicators of compromise (IoCs) in campaign infrastructure ^[5].

3. Platform and policy checks used by journalists and fact‑checkers

Newsrooms and fact‑check units combine digital forensics with platform and policy sleuthing: they verify whether an endorsement appears on a celebrity’s official channels, check advertiser registration and linked domains, and consult regulator registries where scams purport to be financial offerings (The Quint recommends checking RBI/SEBI and celebrities’ official handles) ^[2]. Journalists also rely on platform disclosures and enforcement actions—Forbes and TODAY report that platforms like Meta use automated ad‑review systems and, increasingly, facial recognition to compare suspect ads against celebrities’ profile photos and to remove offending content, with Meta at times referring cases to law enforcement ^{[6] [1] [3]}.

4. Pattern detection and scale: how investigators connect disparate ads

Investigators track repetition and scale by mapping recurring scripts, shared domain names, ad creatives and ad‑account linkages; Unit42 traced the Quantum AI campaign across different domains and found the same threat actor using multiple themes ^[5], and Bitdefender documented near‑identical “exclusive group” giveaway scripts across audio deepfake ads ^[4]. Reuters and Forbes reporting show how fact‑checkers and reporters use ad‑spend footprints and platform takedown logs to estimate scale and to corroborate that content is circulating widely rather than isolated incidents ^[1].

5. Practical detection heuristics fact‑checkers use in reporting

Fact‑check units and investigative reporters emphasize simple, verifiable heuristics before deep technical analysis: check the celebrity’s verified social accounts, hover on links and inspect domain names, and seek corroboration from regulators or the celebrity’s representatives—advice echoed across The Quint and security guides ^{[2] [4]}. When journalists or researchers escalate, they bring in media‑authentication checks (artifact analysis), audio spectral analysis for cloned voices, and infrastructure tracing to link ads to ad accounts and hosting providers ^{[5] [4]}.

6. Gaps, agendas and caveats in the record

The coverage reflects strengths but also limits: security vendors publish technically rich reports that double as alerts but have commercial incentives ^{[4] [5]}, platforms report policy fixes such as facial recognition but face scrutiny over efficacy and privacy tradeoffs ^[6], and newsroom fact‑checks document scams and removals but cannot always quantify losses or identify all actors—reporters commonly rely on platform cooperation and third‑party forensics to make definitive attributions ^{[1] [3]}.