The library.
Everything we publish in one place: white papers, articles, and short notes. Search by keyword or filter by topic and format.
6 results
Detectors collapse from near-perfect to near-random.
An initial benchmark and robustness re-evaluation of leading open-source deepfake detectors, stratified across generators, platform degradations, and demographic groups.
Why an AUC near 1.0 is usually a confound, not a result.
When a detector scores almost perfectly on a single dataset, the likeliest explanation is that it learned the dataset, not the attack.
Report the worst group, not the average.
Why a pooled accuracy number hides the subgroup failures that matter most for fraud and fairness.
The deepfake-hiring pipeline, and what detectors miss.
How synthetic candidates clear interviews, and where the detection layer tends to break.
What a detector sees, and why compression fools it.
A plain-language look at how platform re-encoding erases the very artifacts a detector relies on.
How a deepfake cleared a live video identity check.
A walkthrough of a synthetic-media fraud pattern and the verification gap that let it through.
Field notes
Quarterly changes in attacker tooling, in your inbox.
A short briefing on shifts in attacker tooling, four times a year.