On the Study of Biometric Spoofing Detection using Deep Learning
Quick Answer
This study evaluates deep learning models for biometric spoofing detection, finding MobileNetV2 most effective with 92% accuracy.
Quick Take
This study evaluates deep learning models for biometric spoofing detection, finding MobileNetV2 most effective with 92% accuracy. Inception-v3 shows moderate robustness, while DenseNet-121 and STD struggle with generalization, highlighting the need for improved domain adaptation in biometric security.
Key Points
- MobileNetV2 achieves 92% accuracy in detecting spoofing attacks.
- Inception-v3 shows moderate robustness in facial recognition systems.
- DenseNet-121 and STD struggle with generalization across datasets.
- Study uses CelebA-Spoof and MSU-MFSD datasets for evaluation.
- Highlights need for advances in hybrid architectures for security.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 11505v1 Announce Type: new Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models, MobileNetV2, DenseNet-121, Inception-v3, and Spoof Trace Disentanglement (STD) in detecting spoofing attacks within facial recognition systems.
Using the CelebA-Spoof dataset, the study evaluates model effectiveness using metrics such as accuracy, precision, recall, and F1 Score. Cross-dataset validation is carried out on the MSU-MFSD dataset to assess generalizability. The results show MobileNetV2 as the most efficient model, achieving 92% accuracy while balancing computational effectiveness, making it appropriate for real-life applications. Inception-v3 shows moderate robustness, while DenseNet-121 and STD struggle with generalization.
The findings highlight the need for advances in domain adaptation and hybrid architectures to enhance biometric security systems.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.
