基于Swin Transformer的深度伪造图像检测方法，提升模型泛化能力。
Data-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
- 采用Swin Transformer V2-B架构进行图像分类
- 结合在线数据增强与离线样本生成，丰富训练数据
- 在2024全球竞赛中获优秀奖，验证方法有效性
随着AI技术快速发展，深度伪造技术成为双刃剑，既催生大量生成内容，也对数字安全构成前所未有的挑战。本次竞赛任务是判断人脸图像是否为深度伪造图像，并输出其伪造概率得分。在图像赛道中，我们的方法基于Swin Transformer V2-B分类网络，结合在线数据增强与离线样本生成策略，提升训练样本多样性，增强模型泛化能力。最终在2024年全球深度伪造图像检测挑战赛中获得优秀奖。
原文摘要 · Abstract (English)
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.
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