How Close are Other Computer Vision Tasks to Deepfake Detection?

• URL: http://arxiv.org/abs/2310.00922v1
• Date: Mon, 2 Oct 2023 06:32:35 GMT
• Title: How Close are Other Computer Vision Tasks to Deepfake Detection?
• Authors: Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
• Abstract summary: We present a new measurement, "model separability," for assessing a model's raw capacity to separate data in an unsupervised manner. Our analysis shows that pre-trained face recognition models are more closely related to deepfake detection than other models. We found that self-supervised models deliver the best results, but there is a risk of overfitting.
• Score: 42.79190870582115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake detection. We present a new measurement, "model separability," for visually and quantitatively assessing a model's raw capacity to separate data in an unsupervised manner. We also present a systematic benchmark for determining the correlation between deepfake detection and other computer vision tasks using pre-trained models. Our analysis shows that pre-trained face recognition models are more closely related to deepfake detection than other models. Additionally, models trained using self-supervised methods are more effective in separation than those trained using supervised methods. After fine-tuning all models on a small deepfake dataset, we found that self-supervised models deliver the best results, but there is a risk of overfitting. Our results provide valuable insights that should help researchers and practitioners develop more effective deepfake detection models.

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