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ASTRID Marcella

ORCID
0000-0003-1432-6661
Main Referenced Co-authors
AOUADA, Djamila  (8)
GHORBEL, Enjie  (4)
Lee, Seung-Ik (3)
KACEM, Anis  (2)
SHABAYEK, Abd El Rahman  (2)
Main Referenced Keywords
augmentation (3); Computer Science - Computer Vision and Pattern Recognition (3); Computer Science - Multimedia (2); Computer Science - Sound (2); eess.AS (2);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (4)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > CVI² - Computer Vision Imaging & Machine Intelligence (2)
Main Referenced Disciplines
Computer science (10)
Engineering, computing & technology: Multidisciplinary, general & others (1)
Space science, astronomy & astrophysics (1)

Publications (total 10)

The most downloaded
83 downloads
SHNEIDER, C., SINHA, N., JAMROZIK, M. L., ASTRID, M., ROSTAMI ABENDANSARI, P., KACEM, A., SHABAYEK, A. E. R., & AOUADA, D. (19 April 2023). Compression of Deep Neural Networks for Space Autonomous Systems [Poster presentation]. Luxembourg Space Resources Week 2023, Luxembourg, Luxembourg. doi:10.5281/zenodo.7931156 https://hdl.handle.net/10993/56045

The most cited

15 citations (Scopus®)

Zaheer, M. Z., Mahmood, A., ASTRID, M., & Lee, S.-I. (2023). Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2023.3274611 https://hdl.handle.net/10993/55901

ASTRID, M., SHABAYEK, A. E. R., & AOUADA, D. (08 September 2025). Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge [Paper presentation]. The 33rd European Signal Processing Conference (EUSIPCO 2025), Palermo, Italy.
Peer reviewed

ASTRID, M., GHORBEL, E., & AOUADA, D. (2025). Audio-Visual Deepfake Detection With Local Temporal Inconsistencies. IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings. doi:10.1109/ICASSP49660.2025.10889087
Peer reviewed

ASTRID, M., GHORBEL, E., & AOUADA, D. (October 2024). Statistics-aware Audio-visual Deepfake Detector [Paper presentation]. IEEE International Conference on Image Processing (ICIP 2024), Abu Dhabi, United Arab Emirates.
Peer reviewed

ASTRID, M., GHORBEL, E., & AOUADA, D. (August 2024). Targeted Augmented Data for Audio Deepfake Detection [Paper presentation]. 32nd European Signal Processing Conference (EUSIPCO 2024), Lyon, France.
Peer reviewed

NGUYEN, V. D., MEJRI, N., SINGH, I. P., KULESHOVA, P., ASTRID, M., KACEM, A., GHORBEL, E., & AOUADA, D. (2024). LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection. In LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection (pp. 17395-17405). Seattle, WA, USA, Unknown/unspecified: The IEEE/CVF. doi:10.1109/CVPR52733.2024.01647
Editorial reviewed

ASTRID, M., Zaheer, M. Z., AOUADA, D., & Lee, S.-I. (2024). Exploiting autoencoder’s weakness to generate pseudo anomalies. Neural Computing and Applications. doi:10.1007/s00521-024-09790-z
Peer Reviewed verified by ORBi

ASTRID, M., GHORBEL, E., & AOUADA, D. (2024). Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies [Paper presentation]. British Machine Vision Conference, Glasgow, United Kingdom.
Peer reviewed

ASTRID, M., & Lee, S.-I. (29 October 2023). Assembling Three One-Camera Images for Three-Camera Intersection Classification. ETRI Journal, 45 (5), 862-873. doi:10.4218/etrij.2023-0100
Peer Reviewed verified by ORBi

SHNEIDER, C., SINHA, N., JAMROZIK, M. L., ASTRID, M., ROSTAMI ABENDANSARI, P., KACEM, A., SHABAYEK, A. E. R., & AOUADA, D. (19 April 2023). Compression of Deep Neural Networks for Space Autonomous Systems [Poster presentation]. Luxembourg Space Resources Week 2023, Luxembourg, Luxembourg. doi:10.5281/zenodo.7931156

Zaheer, M. Z., Mahmood, A., ASTRID, M., & Lee, S.-I. (2023). Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2023.3274611
Peer Reviewed verified by ORBi

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