Deepfake Detection in Super-Recognizers and Police Officers
Bibliographic record
Abstract
The present study is the first empirical investigation of the relationshion between human deepfake detection performance (DDP) and individuals' face identity processing ability. Using videos from the Deepfake Detection Challenge, we investigated DDP in two unique observer groups: Super-Recognizers (SRs) and "normal" officers from within the 18K members of the Berlin Police. SRs were identified either via previously proposed lab-based procedures or the only existing tool for SR identification involving increasingly challenging, authentic forensic material: the Berlin Test For Super-Recognizer Identification (beSure®). Participants judged either pairs of videos, or single videos in a 2-alternative forced-choice decision setting (i.e., which of the pair, or whether a single video was a deepfake or not). We explored speed-accuracy trade-offs, compared DDP between lab-identified SRs and non-SRs, and police officers as a function of their independently measured face identity processing (FIP) ability. Interestingly, we found no relationship between DDP and FIP ability. Further work using static deepfakes created with current state-of-the-art generative models is needed to determine the value of SR deployment for deepfake detection in law enforcement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".