論文情報

高瀬 俊希，山内 悠嗣，“自己教師あり学習を導入したWavelet Vision TransformerによるDeepfake検出の高精度化”，精密工学会誌，2025．paper

概要

The proliferation of deepfake technology, leveraging deep learning algorithms to manipulate facial features, attributes, and expressions in images, has elicited significant apprehension. Consequently, a burgeoning body of research aims at identifying images synthesized by deepfake algorithms. Although Vision Transformer-based methods have showcased commendable performance in image recognition, recent investigations suggest a decline in deepfake detection compared to convolutional neural network-based techniques. This study, proposes a high-precision deepfake detection approach employing the Wavelet Vision Transformer, incorporating self-supervised learning. The Wavelet Vision Transformer demonstrates proficiency in capturing essential high-frequency components within images, particularly pertinent for deepfake detection. By amalgamating it with self-supervised learning, a variant of representation learning, our method facilitates the precise detection of manipulation artifacts within deepfake images, thereby attaining elevated detection accuracy.

Bibtex Reference

@article{高瀬2025,
  title={{自己教師あり学習を導入したWavelet Vision TransformerによるDeepfake検出の高精度化}},
  author={高瀬 俊希 and 山内 悠嗣},
  journal={精密工学会誌},
  volume={91},
  number={2},
  pages={156-162},
  year={2025},
  doi={10.2493/jjspe.91.156}
}