TRANSFORMER AND NODE-COMPRESSED DNN BASED DUAL-PATH SYSTEM FOR MANIPULATED FACE DETECTION

Zhengbo Luo, Sei Ichiro Kamata, Zitang Sun

研究成果: Conference contribution

抄録

Deep neural networks (DNNs) have extensively promoted data generation development; the quality of these generated content has achieved an impressive new level. Therefore, manipulated content, especially facial manipulation, is a growing concern for online information legitimacy. Most current deep learning-based methods depend on local features sampled by convolutional kernels and lack knowledge globally. To address the problem, we propose a dual-path pipeline using Neural Ordinary Differential Equations (NODE) based neural network and facial-feature biased transformer to deal with the visual content from a different view. The transformer path could link these landmarks in a long-range, moreover, we adopt an attention guided augmentation based self-ensemble for more robust performance. Extensive experiments show that our system could surpass several commonly used approaches in terms of video-level accuracy and AUC with better interpretability.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
出版社IEEE Computer Society
ページ3882-3886
ページ数5
ISBN(電子版)9781665441155
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
継続期間: 2021 9月 192021 9月 22

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2021-September
ISSN(印刷版)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
国/地域United States
CityAnchorage
Period21/9/1921/9/22

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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