RESEARCH INTERESTS

Research Areas

Face Recognition

Focus on robust and scalable facial identification under real-world conditions.

Face Presentation Attack Detection

Detect spoofing attempts like masks, printed photos, or video replays.

Deepfake & Face Manipulation Detection

Classify manipulated content using one-class and open-set learning.

One-Class & Open-Set Recognition

Develop models that perform detection or classification when only “normal” data is available at training time.

Ensemble Learning

Develop fusion systems to combine multiple sources of information at various levels, e.g. soft decision-level, feature-level, etc. for decision making.

Key Research Projects

Deepfake Detection Using Deep One-Class and Open-Set Recognition

Developed novel deep learning models that detect manipulated faces even under unknown attack types.

Selected Publications

See Publications page for a full list.

1. S. Nourmohammadi, Arashloo, S.R., and Kittler, J., “ℓp-Norm Constrained One-Class Classifier Combination”, Information Fusion, Elsevier, vol. 114, 102700, 2025.

2. Arashloo, S.R., “Large-Margin Multiple Kernel ℓp-SVDD Using Frank-Wolfe Algorithm for Novelty Detection”, Pattern Recognition, Elsevier, vol. 148, 110189, 2024

3. Arashloo, S.R., “One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach”, Image Processing, IEEE Transactions on, vol. 32, pp. 1843- 1856, 2023.

4. Arashloo, S.R., “Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels”, Information Forensics and Security, IEEE Transactions on, vol. 18, pp. 1421-1432, 2023.

5. Arashloo, S.R. and Kittler, J., “Robust One-Class Kernel Spectral Regression”, Neural Networks and Learning Systems, IEEE Transactions on, vol. 32, no. 3, pp. 999-1013, Mar. 2021.

Ongoing & Future Directions

Semi-Supervised Deepfake Detection: training detectors with limited labels

Sparsity-induced Classifier Fusion

Anomaly Detection in Sequential Data Streams