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Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection

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Abstract

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.

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Data Availibility Statement

All the datasets used in this paper are available online. FaceForensics++ (FF++) (https://github.com/ondyari/FaceForensics), Celeb-DF (CDF) (https://github.com/yuezunli/celeb-deepfakeforensics), DFDC (https://ai.meta.com/datasets/dfdc/), DeeperForensics (DFo) (https://liming-jiang.com/projects/DrF1/DrF1.html), FaceShifter(Fsh) (https://github.com/ondyari/FaceForensics), Wild-Deepfake (Wild-DF) (https://github.com/deepfakeinthewild/deepfake-in-the-wild), KoDF (https://deepbrainai-research.github.io/kodf/), and Deepfakes LSUN-Bedroom (DBL) (https://github.com/jonasricker/diffusion-model-deepfake-detection#dataset) can be downloaded from their official website accordingly.

Notes

  1. The model trained on the FaceForensics++ dataset and evaluated on Celeb-DF, the same applies to the following text.

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Acknowledgements

The authors would like to thank the reviewers and the associate editor for their valuable comments. The authors also thank Ziming Yang, and Gengyun Jia for their help in improving the technical writing aspect of this paper. This work was supported by the National Natural Science Foundation of China (NSFC) under Grants (62376265, 62276256 and U21B2045), the Beijing Nova Program under Grant Z211100002121108, and the Young Elite Scientists Sponsorship Program by CAST.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Yuting Xu & Xiao-Yu Zhang

  2. CRIPAC and MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jian Liang & Lijun Sheng

  3. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Yuting Xu & Xiao-Yu Zhang

  4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Jian Liang

  5. Department of Automation, University of Science and Technology of China, Beijing, China

    Lijun Sheng

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Xu, Y., Liang, J., Sheng, L. et al. Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection. Int J Comput Vis 132, 5663–5680 (2024). https://doi.org/10.1007/s11263-024-02054-2

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