2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSS-MMSDF-2.4
Paper Title TRANSFORMER AND NODE-COMPRESSED DNN BASED DUAL-PATH SYSTEM FOR MANIPULATED FACE DETECTION
Authors Zhengbo Luo, Sei-ichiro Kamata, Zitang Sun, Waseda University, Japan
SessionSS-MMSDF-2: Special Session: AI for Multimedia Security and Deepfake 2
LocationArea A
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for information forensics and security
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 of 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. Moreover, we adopt an attention guided augmentation based self-ensemble for more robust performance. Extensive experiments show that our system could surpass several state-of-the-art level approaches in terms of video-level accuracy and AUC with better interpretability.