Abstract
The proliferation of deepfake technology poses significant challenges to the authenticity and trustworthiness of digital media, necessitating the development of robust detection methods. This study explores the application of Swin Transformers, a state-of-the-art architecture leveraging shifted windows for self-attention, in detecting and classifying deepfake images. Using the Real and Fake Face Detection dataset by Yonsei University's Computational Intelligence Photography Lab, we evaluate the Swin Transformer and hybrid models such as Swin-ResNet and Swin-KNN, focusing on their ability to identify subtle manipulation artifacts. Our results demonstrate that the Swin Transformer outperforms conventional CNN-based architectures, including VGG16, ResNet18, and AlexNet, achieving a test accuracy of 71.29\%. Additionally, we present insights into hybrid model design, highlighting the complementary strengths of transformer and CNN-based approaches in deepfake detection. This study underscores the potential of transformer-based architectures for improving accuracy and generalizability in image-based manipulation detection, paving the way for more effective countermeasures against deepfake threats.
Abstract (translated)
深度伪造技术的扩散对数字媒体的真实性和可信度构成了重大挑战，这促使开发出更为强大的检测方法。本研究探索了Swin Transformer的应用，这是一种采用移位窗口进行自注意力处理的先进架构，在检测和分类深度伪造图像方面具有显著效果。我们使用延世大学计算智能摄影实验室提供的“真实与虚假人脸检测”数据集，评估了Swin Transformer及其混合模型（如Swin-ResNet和Swin-KNN）在识别细微操作痕迹方面的表现。实验结果表明，Swin Transformer的性能超越了传统的基于卷积神经网络（CNN）的架构，包括VGG16、ResNet18和AlexNet，在测试中达到了71.29%的准确率。 此外，我们还探讨了混合模型设计的见解，突显了在深度伪造检测方面，变换器与基于CNN的方法之间的互补优势。这项研究强调了基于变换器架构对于改善图像操作检测中的准确性及通用性所具有的潜力，并为更有效的应对深度伪造威胁铺平道路。
URL
https://arxiv.org/abs/2501.15656
