Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced users. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. Indeed, discriminating a Deepfake from a real image could be a difficult task even for human eyes but recent works are trying to apply the same technology used for generating images for discriminating them with preliminary good results but with many limitations: employed Convolutional Neural Networks are not so robust, demonstrate to be specific to the context and tend to extract semantics from images. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed. The method is based on the Expectation-Maximization algorithm trained to detect and extract a fingerprint that represents the Convolutional Traces (CT) left by GANs during image generation. The CT demonstrates to have high discriminative power achieving better results than state-of-the-art in the Deepfake detection task also proving to be robust to different attacks. Achieving an overall classification accuracy of over 98%, considering Deepfakes from 10 different GAN architectures not only involved in images of faces, the CT demonstrates to be reliable and without any dependence on image semantic. Finally, tests carried out on Deepfakes generated by FACEAPP achieving 93% of accuracy in the fake detection task, demonstrated the effectiveness of the proposed technique on a real-case scenario.

中文翻译：

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通过暴露图像上的卷积痕迹来对抗 Deepfake

人工智能和图像处理的进步正在改变人们与数字图像和视频交互的方式。FACEAPP 等广泛使用的移动应用程序利用最先进的生成对抗网络 (GAN) 对人脸照片进行极端转换，例如性别互换、衰老等。结果完全真实，即使对于没有经验的用户也极易被利用. 这种媒体对象取了 Deepfake 的名字，并在多媒体取证领域提出了一个新的挑战：Deepfake 检测挑战。事实上，即使对于人眼来说，从真实图像中区分 Deepfake 也可能是一项艰巨的任务，但最近的工作正试图应用与生成图像相同的技术来区分它们，初步取得了良好的结果，但存在许多局限性：使用的卷积神经网络不是那么健壮，证明是特定于上下文的，并且倾向于从图像中提取语义。在本文中，提出了一种旨在从图像中提取 Deepfake 指纹的新方法。该方法基于经过训练的期望最大化算法，用于检测和提取代表 GAN 在图像生成过程中留下的卷积轨迹 (CT) 的指纹。CT 证明具有高判别能力，在 Deepfake 检测任务中取得比最先进技术更好的结果，也证明对不同的攻击具有鲁棒性。实现超过 98% 的整体分类准确率，考虑到来自 10 种不同 GAN 架构的 Deepfakes 不仅涉及面部图像，CT 证明是可靠的，并且不依赖于图像语义。最后，