Mr. Wassim Hamidouche Profile

Wassim Hamidouche

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Publications (2)

SPIE Journal Paper | 19 October 2023

On the effectiveness of handcrafted features for deepfake video detection

Bachir Kaddar, Sid Ahmed Fezza, Wassim Hamidouche, Zahid Akhtar, Abdenour Hadid

JEI, Vol. 32, Issue 05, 053033, (October 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.5.053033

KEYWORDS: Video, Eye, Mouth, Feature extraction, Education and training, Histograms of oriented gradient, Facial recognition systems, Matrices, Deep learning, Optical flow

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Recent developments in advanced generative deep learning techniques have led to considerable progress in deepfake technology. CNN-based deepfake detection approaches have demonstrated superior performance. The ability to learn meaningful representations generated by convolutional multilayer nonlinear structures is the key to success. However, the black-box nature of such approaches has been a major concern for exploring hidden and complex characteristics as well as potential limitations of CNN-based models. To gain insights into the scope of the deepfake detection task, we investigate the effectiveness of handcrafted feature-based methods for deepfake video detection. First, we experiment with six top-performing handcrafted descriptors to extract the discriminating image features and then train SVMs on the extracted features to learn a suitable model. We also study the effect of selecting specific facial components on the detection performance. Specifically, we consider features extracted from the left eye, right eye, mouth, and entire face. Moreover, we propose a combination of these features and highlight the importance of this combination in terms of detection performance. Experimental results show that the SIFT feature descriptor achieves the best performance on deepfake videos generated by the neural texture technique, with a detection accuracy of 83.50%, which is better than deep learning-based methods. This is in contrast to the conventional understanding that deep learning methods systematically outperform handcrafted feature-based approaches. In addition, the obtained results on the FaceForensics++ dataset highlight the benefit of using some facial components to further boost the detection performance. Moreover, motivated by the effectiveness of the LBPTOP and SIFT in the deepfake detection task, we combined the LBPTOP and SIFT to best characterize the specific spatiotemporal inconsistencies commonly found in fake videos for boosting deepfake detection performance. Finally, we show the strengths and weaknesses of methods based on handcrafted features for deepfake detection and provide directions for future research.

Proceedings Article | 1 August 2021 Presentation + Paper

Implementation of film-grain technology within VVC

Miloš Radosavljevic, Edouard François, Erik Reinhard, Wassim Hamidouche, Thomas Amestoy

Proceedings Volume 11842, 118420D (2021) https://doi.org/10.1117/12.2594084

KEYWORDS: Video, Video compression, Image processing

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