Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
This addresses digital security concerns by improving detection of AI-generated fake images, but it is incremental as it builds on existing methods for a specific challenge.
The paper tackled the problem of detecting deepfake images in a competition, using a Swin Transformer V2-B network with data augmentation to achieve an excellence award.
With the rapid development of technology in the field of AI, deepfake technology has emerged as a double-edged sword. It has not only created a large amount of AI-generated content but also posed unprecedented challenges to digital security. The task of the competition is to determine whether a face image is a Deepfake image and output its probability score of being a Deepfake image. In the image track competition, our approach is based on the Swin Transformer V2-B classification network. And online data augmentation and offline sample generation methods are employed to enrich the diversity of training samples and increase the generalization ability of the model. Finally, we got the award of excellence in Deepfake image detection.