Deepfake Detection: A Comparative Analysis
It addresses the problem of deepfake detection for individuals and society, but it is incremental as it focuses on comparing existing methods without introducing new techniques.
This paper conducted a comparative analysis of supervised and self-supervised models for deepfake detection, evaluating eight supervised architectures and two transformer-based models on four benchmarks to provide insights into effectiveness and generalization.
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.