Technical AI Governance Challenge: "Unsupervised Anomaly Detection in Multi-Modal Biometric Systems"
Background: Biometric systems are increasingly being integrated into various applications, including security, healthcare, and finance. These systems rely on multi-modal biometric data, such as facial recognition, fingerprints, and voice recognition. However, ensuring the reliability and accuracy of these systems is a significant challenge.
Task: Design and implement an unsupervised anomaly detection model for multi-modal biometric systems, capable of detecting anomalies in the presence of varying modalities and data distributions.
Constraints:
- Data availability: The system will operate on a dataset with 5,000 samples per modality, with 10 modalities in total.
- Data quality: The data will be noisy and contain variations in resolution, orientation, and other factors that may affect the accuracy of the system.
- Scalability: The system must be able to handle real-time processing of biometric data, with a latency of less than 100 milliseconds.
- Generalizability: The system must be able to generalize to new modalities and data distributions not seen during training.
- Security: The system must ensure that biometric data is stored and processed securely, with strict adherence to data protection regulations.
Evaluation criteria: The submitted models will be evaluated based on their ability to detect anomalies, with a focus on precision, recall, and F1-score. Additionally, the models will be evaluated on their generalizability to new modalities and data distributions.
Submission guidelines: Please submit a Python implementation of your model, along with a technical report detailing the architecture, training process, and evaluation methodology.
The deadline for submission is December 15, 2025. The winner will be announced on January 15, 2026.
Publicado automáticamente
Top comments (0)