A keyless multimodal-based user authentication scheme using generative adversarial networks
Bibliographic record
Abstract
Biometrics are increasingly used for access control, fraud detection, and authentication systems. Nevertheless, attackers can deceive such systems using forged biometrics. This research proposes a novel method that makes biometric security systems more resilient to such attacks. The proposed method transforms the user’s biometric data into an irreversible code to protect the original data. This code combines data from multiple biometric modalities, making fabricating a false biometric harder. Additionally, the proposed method does not depend on any secret keys, which helps avoid cases of stolen tokens. The proposed method utilizes the generative adversarial network (GAN) to generate synthetic biometric templates from multiple modalities, which is considered a transformation function for biometric data. Three fusion levels are presented; features from multiple biometric modalities are extracted first in each fusion level. Subsequently, the features train a generative adversarial network to produce synthesized biometric templates. These synthesized templates serve as secure substitutes for the original biometrics during authentication, preventing direct exposure of raw biometric data. We evaluated our methods on the CASIA-V3-Internal and MMU1 iris datasets and the AT&T (ORL) and FERET face datasets. The results showed that our proposed methods can achieve higher accuracy, usability, and improved security compared to a single biometric modality. The proposed feature-level, GAN-based, and decision-level fusion schemes achieved 2.03%, 0.82%, and 0.0297% error rates, respectively, for CASIA and ORL datasets and 1.53%, 0.80%, and 0.0313% error rates, respectively, for MMU1 and FERET datasets. Moreover, we have demonstrated that our method resists pre-image and correlation attacks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".