Advanced manipulation techniques have provided criminals with opportunities
to make social panic or gain illicit profits through the generation of
deceptive media, such as forged face images. In response, various deepfake
detection methods have been proposed to assess image authenticity. Sequential
deepfake detection, which is an extension of deepfake detection, aims to
identify forged facial regions with the correct sequence for recovery.
Nonetheless, due to the different combinations of spatial and sequential
manipulations, forged face images exhibit substantial discrepancies that
severely impact detection performance. Additionally, the recovery of forged
images requires knowledge of the manipulation model to implement inverse
transformations, which is difficult to ascertain as relevant techniques are
often concealed by attackers. To address these issues, we propose
Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various
spatial scales and sequential permutations in forged face images and achieve
recovery without requiring knowledge of the corresponding manipulation method.
Furthermore, existing evaluation metrics only consider detection accuracy at a
single inferring step, without accounting for the matching degree with
ground-truth under continuous multiple steps. To overcome this limitation, we
propose a novel evaluation metric called Complete Sequence Matching (CSM),
which considers the detection accuracy at multiple inferring steps, reflecting
the ability to detect integrally forged sequences. Extensive experiments on
several typical datasets demonstrate that MMNet achieves state-of-the-art
detection performance and independent recovery performance.

提出了一种名为 Multi-Collaboration and Multi-Supervision Network (MMNet) 的方法，用于处理伪造的人脸图像中的各种空间尺度和顺序排列，并在不需要相关操作方法知识的情况下实现恢复，同时提出了一种名为 Complete Sequence Matching (CSM) 的新的评估指标，反映了在多个推断步骤中检测完整伪造序列的能力。在多个典型数据集上的广泛实验表明，MMNet 在检测性能和独立恢复性能方面达到了最先进的水平。