DepthFake：一种基于深度信息的Deepfake视频检测策略
DepthFake: a depth-based strategy for detecting Deepfake videos
AI总结：
针对深度伪造视频检测问题，提出DepthFake方法，通过单目深度估计获取深度图结合RGB信息构建RGBD检测架构，在FaceForensic++数据集上实现最高11.7%的性能提升。
摘要：
近年来，虚假内容以惊人的速度增长。社交媒体与网络平台的普及，使得恶意行为者能够越来越轻易地大规模传播这类内容。与此同时，随着虚假图像生成方法的日益普及，学界已提出许多基于深度学习的检测技术。其中大多数方法都依赖从RGB图像中提取显著特征，再通过二分类器判断图像的真伪。\n本文提出了DepthFake，一项研究如何利用深度图改进传统基于RGB的检测方法的研究。深度信息通过最新的单目深度估计技术从RGB图像中提取。本文在鲁棒的预训练架构上，验证了深度图对深度伪造检测任务的有效增益。在FaceForensic++数据集上，相较于标准RGB架构，所提出的RGBD方法平均性能提升3.20%，针对部分深度伪造攻击的提升最高可达11.7%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
