  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.
