Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images/videos. 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 face recognition or face detection, is unfortunately paid little attention to in literature for face manipulation detection. In this paper, we explore the possibility of incorporating the face depth map as auxiliary information for robust face manipulation detection. To this end, we first propose a Face Depth Map Transformer (FDMT) to estimate the face depth map patch by patch from an 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. We also propose an RGB-Depth Inconsistency Attention (RDIA) module to effectively capture the inter-frame inconsistency for multi-frame input. Various experiments demonstrate the advantage of our proposed method for face manipulation detection.

翻译：人脸篡改检测对于人脸图像/视频的可靠性与安全性具有重要意义。近期研究侧重于利用辅助信息或先验知识捕捉鲁棒的篡改痕迹，已显示出良好前景。作为重要的人脸特征之一，人脸深度图在人脸识别、人脸检测等其他领域已被证明有效，但在人脸篡改检测的文献中却鲜少受到关注。本文探索将人脸深度图作为辅助信息用于鲁棒人脸篡改检测的可能性。为此，我们首先提出一种人脸深度图变换器（FDMT），通过分块处理从RGB人脸图像估计人脸深度图，该方法能够捕捉因篡改产生的局部深度异常。随后，将估计的人脸深度图作为辅助信息，通过新设计的多头深度注意力（MDA）机制与骨干网络特征进行融合。我们还提出RGB-深度不一致性注意力（RDIA）模块，以有效捕捉多帧输入时的帧间不一致性。大量实验证明了我们所提方法在人脸篡改检测中的优势。