Directed Adversarial Attacks on Fingerprints using Attributions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fingerprint recognition systems verify the identity of individuals and provide access to secure information in various commercial applications. However, with advancements in artificial intelligence, fingerprint-based security methods are vulnerable to attack. Such a breach has the potential to compromise confidential, private and valuable information. In this paper, we attack a state-of-the-art fingerprint recognition system based on transfer learning. Our approach uses attribution analysis to identify the fingerprint region crucial to correct classification, and then perturbs the fingerprint using error masks derived from a neural network to generate an adversarial fingerprint. Image quality assessment metrics applied to calculate the difference between the original and perturbed fingerprints include average difference, maximum difference, normalized absolute error, and peak signal to noise ratio. On the ATVS fingerprint dataset, the differences between these values in the original and corresponding perturbed fingerprint images are negligible. Further, the VeriFinger SDK is used to detect the minutiae and perform matching between the original and perturbed fingerprints. The matching score is above 250, which reinforces the fact that there is virtually no loss between the original and perturbed fingerprints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it