  The deepfake threats to society and cybersecurity have provoked significant
public apprehension, driving intensified efforts within the realm of deepfake
video detection. Current video-level methods are mostly based on {3D CNNs}
resulting in high computational demands, although have achieved good
performance. This paper introduces an elegantly simple yet effective strategy
named Thumbnail Layout (TALL), which transforms a video clip into a pre-defined
layout to realize the preservation of spatial and temporal dependencies. This
transformation process involves sequentially masking frames at the same
positions within each frame. These frames are then resized into sub-frames and
reorganized into the predetermined layout, forming thumbnails. TALL is
model-agnostic and has remarkable simplicity, necessitating only minimal code
modifications. Furthermore, we introduce a graph reasoning block (GRB) and
semantic consistency (SC) loss to strengthen TALL, culminating in TALL++. GRB
enhances interactions between different semantic regions to capture
semantic-level inconsistency clues. The semantic consistency loss imposes
consistency constraints on semantic features to improve model generalization
ability. Extensive experiments on intra-dataset, cross-dataset,
diffusion-generated image detection, and deepfake generation method recognition
show that TALL++ achieves results surpassing or comparable to the
state-of-the-art methods, demonstrating the effectiveness of our approaches for
various deepfake detection problems. The code is available at
https://github.com/rainy-xu/TALL4Deepfake.
