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Fusion-SSAT: Unleashing the Potential of Self-supervised Auxiliary Task by Feature Fusion for Generalized Deepfake Detection

arXiv 2601.00789 ↗First posted 2 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

In this work, we attempted to unleash the potential of self-supervised learning as an auxiliary task that can optimise the primary task of generalised deepfake detection. To explore this, we examined different combinations of the training schemes for these tasks that can be most effective. Our findings reveal that fusing the feature representation from self-supervised auxiliary tasks is a powerful feature representation for the problem at hand. Such a representation can leverage the ultimate potential and bring in a unique representation of both the self-supervised and primary tasks, achieving better performance for the primary task. We experimented on a large set of datasets, which includes DF40, FaceForensics++, Celeb-DF, DFD, FaceShifter, UADFV, and our results showed better generalizability on cross-dataset evaluation when compared with current state-of-the-art detectors.

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References · 66

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  7. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  8. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  9. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  10. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  11. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  12. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
38 more
  1. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  2. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  3. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,322
  4. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
  5. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,101
  6. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
  7. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 991
  8. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  9. 2020A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wildcited by 672
  10. 2024Open-Sora: Democratizing Efficient Video Production for Allcited by 652
  11. 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 575
  12. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 523
  13. 2019Face X-ray for More General Face Forgery Detectioncited by 470
  14. 2018MesoNet: a Compact Facial Video Forgery Detection Networkcited by 409
  15. 2022SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animationcited by 382
  16. 2022Detecting Deepfakes with Self-Blended Imagescited by 338
  17. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 319
  18. 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 297
  19. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 262
  20. 2019On the Detection of Digital Face Manipulationcited by 260
  21. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 244
  22. 2021SimSwap: An Efficient Framework For High Fidelity Face Swappingcited by 243
  23. 2020Self-supervised Co-training for Video Representation Learningcited by 232
  24. 2021PIRenderer: Controllable Portrait Image Generation via Semantic Neural Renderingcited by 203
  25. 2018Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videoscited by 180
  26. 2022Depth-Aware Generative Adversarial Network for Talking Head Video Generationcited by 178
  27. 2022Latent Image Animator: Learning to Animate Images via Latent Space Navigationcited by 175
  28. 2023DeepfakeBench: A Comprehensive Benchmark of Deepfake Detectioncited by 166
  29. 2020Partial FC: Training 10 Million Identities on a Single Machinecited by 163
  30. 2024DF40: Toward Next-Generation Deepfake Detectioncited by 129
  31. 2021Representative Forgery Mining for Fake Face Detectioncited by 108
  32. 2024LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detectioncited by 89
  33. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 85
  34. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 74
  35. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  36. 2023BlendFace: Re-designing Identity Encoders for Face-Swappingcited by 66
  37. 2021Self-supervised Video Transformercited by 64
  38. 2024Exploiting Style Latent Flows for Generalizing Deepfake Video Detectioncited by 59

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