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All Outputs (2)

Unmasking the imposters: task-specific feature learning for face presentation attack detection.
Presentation / Conference Contribution
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2023. Unmasking the imposters: task-specific feature learning for face presentation attack detection. In Proceedings of the 2023 International joint conference on neural networks (IJCNN2023), 18-23 June 2023, Gold Coast, Australia. Piscataway: IEEE [online], 10191953. Available from: https://doi.org/10.1109/IJCNN54540.2023.10191953

Presentation attacks pose a threat to the reliability of face recognition systems. A photograph, a video, or a mask representing an authorised user can be used to circumvent the face recognition system. Recent research has demonstrated high accuracy... Read More about Unmasking the imposters: task-specific feature learning for face presentation attack detection..

Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis.
Presentation / Conference Contribution
ABDULLAKUTTY, F. and NASEEM, U. 2024. Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis. In Chua, T.-S., Ngo, C.-W., Kumar, R., Lauw, H.W. and Lee, R.K.-W. (eds.). WWW'24 companion: companion proceedings of the ACM web conference 2024, 13-17 May 2024, Singapore. New York: ACM [online], pages 1681-1689. Available from: https://doi.org/10.1145/3589335.3652504

Memes are important because they serve as conduits for expressing emotions, opinions, and social commentary online, providing valuable insight into public sentiment, trends, and social interactions. By combining textual and visual elements, multi-mod... Read More about Decoding memes: a comprehensive analysis of late and early fusion models for explainable meme analysis..