Abstract
Deepfakes powered by advanced machine learning models present a significant and evolving threat to identity verification and the authenticity of digital media. Although numerous detectors have been developed to address this problem, their effectiveness has yet to be tested when applied to real-world data. In this work we evaluate modern deepfake detectors, introducing a novel testing procedure designed to mimic real-world scenarios for deepfake detection. Using state-of-the-art deepfake generation methods, we create a comprehensive dataset containing more than 500,000 high-quality deepfake images. Our analysis shows that detecting deepfakes still remains a challenging task. The evaluation shows that in fewer than half of the deepfake detectors tested achieved an AUC score greater than 60%, with the lowest being 50%. We demonstrate that basic image manipulations, such as JPEG compression or image enhancement, can significantly reduce model performance. All code and data are publicly available at this https URL.
Abstract (translated)
由先进机器学习模型驱动的Deepfakes对身份验证和数字媒体的真实性构成了一个重大且不断演变的威胁。尽管已经开发出了多种检测器来解决这一问题，但它们在应用于实际数据时的有效性尚未得到充分测试。在这项工作中，我们评估了现代Deepfake检测器，并引入了一种新颖的测试程序，旨在模拟现实世界中的Deepfake检测场景。使用最先进的Deepfake生成方法，我们创建了一个包含超过50万张高质量Deepfake图像的全面数据集。我们的分析表明，识别Deepfakes仍然是一项具有挑战性的任务。评估结果显示，在接受测试的所有Deepfake检测器中，不足一半达到了60%以上的AUC分数，最低者仅为50%。此外，我们证明了基本的图像操作，如JPEG压缩或图像增强，可以显著降低模型的表现。所有代码和数据均可在以下网址公开获取：[https URL]。
URL
https://arxiv.org/abs/2507.21905
