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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".