Fake content has grown at an incredible rate over the past few years. The spread of social media and online platforms makes their dissemination on a large scale increasingly accessible by malicious actors. In parallel, due to the growing diffusion of fake image generation methods, many Deep Learning-based detection techniques have been proposed. Most of those methods rely on extracting salient features from RGB images to detect through a binary classifier if the image is fake or real. In this paper, we proposed DepthFake, a study on how to improve classical RGB-based approaches with depth-maps. The depth information is extracted from RGB images with recent monocular depth estimation techniques. Here, we demonstrate the effective contribution of depth-maps to the deepfake detection task on robust pre-trained architectures. The proposed RGBD approach is in fact able to achieve an average improvement of 3.20% and up to 11.7% for some deepfake attacks with respect to standard RGB architectures over the FaceForensic++ dataset.
翻译:过去几年来,虚假内容以令人难以置信的速度增长。社交媒体和在线平台的传播使得恶意行为者越来越容易获得其大规模传播。与此同时,由于假造图像生成方法日益普及,提出了许多基于深学习的探测技术。这些方法大多依靠从RGB图像中提取显著特征,以便在图像是假的或真实的情况下通过二进制分类器检测。在本文中,我们建议“深度假”研究如何改进传统的基于RGB的深度映射方法。深度信息是利用最近单眼深度估测技术从 RGB 图像中提取的。在这里,我们展示了深度映射对强健的预训练前建筑的深假探测任务的有效贡献。提议的RGBD方法实际上能够实现3.20%的平均改进,对于Faceforensec+d数据集对标准 RGB结构的深度攻击,平均达到11.7%。