Deepfake Detection: A Comparative Analysis
Sohail Ahmed Khan Duc-Tien Dang-Nguyen
This paper present a comprehensive comparative analysis of supervised and
self-supervised models for deepfake detection. We evaluate eight supervised
deep learning architectures and two transformer-based models pre-trained using
self-supervised strategies (DINO, CLIP) on four benchmarks (FakeAVCeleb,
CelebDF-V2, DFDC, and FaceForensics++). Our analysis includes intra-dataset and
inter-dataset evaluations, examining the best performing models, generalisation
capabilities, and impact of augmentations. We also investigate the trade-off
between model size and performance. Our main goal is to provide insights into
the effectiveness of different deep learning architectures (transformers,
CNNs), training strategies (supervised, self-supervised), and deepfake
detection benchmarks. These insights can help guide the development of more
accurate and reliable deepfake detection systems, which are crucial in
mitigating the harmful impact of deepfakes on individuals and society.