论文标题
探索深度信息以进行面部操纵检测
Exploring Depth Information for Face Manipulation Detection
论文作者
论文摘要
面部操纵检测已引起人们对面部图像的可靠性和安全性的广泛关注。最近的研究着重于使用辅助信息或先验知识来捕获强大的操纵轨迹，这被证明是有希望的。作为重要的面部特征之一，不幸的是，它在诸如面部识别或面部检测等其他领域有效的面部深度图在发现操纵的面部图像的文献中很少关注。在本文中，我们探讨了将面部深度图纳入辅助信息的可能性，以解决现实世界应用中面部操纵检测的问题。为此，我们首先提出了一个面部深度映射变压器（FDMT），以通过从RGB面部图像中贴片来估计面部深度图贴片，该图形能够捕获由于操纵而产生的局部深度异常。然后，估计的面部深度图被视为使用新设计的多头深度注意（MDA）机制将辅助信息与骨干特征集成在一起。各种实验证明了我们提出的面部操纵检测方法的优势。
Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face depth map, which has shown to be effective in other areas such as the face recognition or face detection, is unfortunately paid little attention to in literature for detecting the manipulated face images. In this paper, we explore the possibility of incorporating the face depth map as auxiliary information to tackle the problem of face manipulation detection in real world applications. To this end, we first propose a Face Depth Map Transformer (FDMT) to estimate the face depth map patch by patch from a RGB face image, which is able to capture the local depth anomaly created due to manipulation. The estimated face depth map is then considered as auxiliary information to be integrated with the backbone features using a Multi-head Depth Attention (MDA) mechanism that is newly designed. Various experiments demonstrate the advantage of our proposed method for face manipulation detection.
