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-2.11
Paper Title FAKE FACE DETECTION USING LOCAL BINARY PATTERN AND ENSEMBLE MODELING
Authors Yonghui Wang, Vahid Zarghami, Suxia Cui, Prairie View A&M University, United States
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 Special Sessions: Artificial Intelligence for Multimedia Security and Deepfake
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Fake faces generated with Generative Adversarial Networks (GANs) are becoming more and more realistic and getting harder to be identified directly by human beings. However, CNN (Convolutional Neural network) based deep learning architecture can achieve almost perfect detection accuracy on such fake faces. In this paper we present a study of fake face detection with the exploration of the global texture features based on the empirical knowledge that the textures of fake faces are quite different from those of real faces. A new architecture, LBP (Local Binary Pattern)-Net, is designed to utilize binary representation image texture for the effective identification of fake images. Experimental results show that the proposed method is more robust than existing algorithms for detecting fake images edited by different image augmentation methods, such as blurring, cutout, brightness and color changing, equalization, etc. Ensemble models are also experimented to combine advantages of individual models. The most significant effect of ensemble models is the robustness for detecting edited fake images compared to single models. Experimental results show that our ensemble models outperform single models for detecting fake images.