Investigating the Impact of Inclusion in Face Recognition Training Data
Bibliographic record
Abstract
Modern face recognition systems leverage datasets containing im-ages of hundreds of thousands of individuals’ faces. Recently, therehas been significant public scrutiny into the privacy implications oflarge-scale training datasets such as MS-Celeb-1M, as many peo-ple are uncomfortable with their face being used to train dual-usetechnologies that can enable mass surveillance. However, the im-pact of an individual’s inclusion in training data on a derived sys-tem’s ability to recognize them has not previously been studied. Inthis work, we audit ArcFace, a state-of-the-art, open-source facerecognition system, in a large-scale face identification experiment.We find Rank-1 identification accuracy of 79.71% for individualspresent in training data and 75.73% for those not present. These re-sults demonstrate that modern face recognition systems work bet-ter for individuals they are trained on, which has serious privacyimplications as all large-scale, open-source training datasets do notgather informed consent from individuals during their collection.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".