Detecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation
This addresses the challenge of identifying malicious digital forgeries that threaten social and political stability, though it appears incremental by combining existing techniques.
The study tackled the problem of detecting Deepfake-forged images and videos by proposing a solution using separable convolutional neural networks and image segmentation, achieving excellent performance in detection efficiency.
Recent advances in AI technology have made the forgery of digital images and videos easier, and it has become significantly more difficult to identify such forgeries. These forgeries, if disseminated with malicious intent, can negatively impact social and political stability, and pose significant ethical and legal challenges as well. Deepfake is a variant of auto-encoders that use deep learning techniques to identify and exchange images of a person's face in a picture or film. Deepfake can result in an erosion of public trust in digital images and videos, which has far-reaching effects on political and social stability. This study therefore proposes a solution for facial forgery detection to determine if a picture or film has ever been processed by Deepfake. The proposed solution reaches detection efficiency by using the recently proposed separable convolutional neural network (CNN) and image segmentation. In addition, this study also examined how different image segmentation methods affect detection results. Finally, the ensemble model is used to improve detection capabilities. Experiment results demonstrated the excellent performance of the proposed solution.