Towards Generalizable Deepfake Detection via Real Distribution Bias Correction

arXiv:2603.14005 · cs.CV · Submitted 2026-03-14 · Read on arXiv

cs.CV

Submitted: 2026-03-14

Updated: 2026-09-16

Comments: The authors request withdrawal because the current manuscript requires substantial revision to its theoretical formulation and presentation, beyond the scope of a routine version update. There is currently no replacement version available, and any future work arising from this manuscript may differ substantially in scope and content

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 27× in this paper · explained in 13 episodes
• AUC — 14× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Systematic Bias — 5× in this paper · explained in 3 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generalizability — 4× in this paper · explained in 6 episodes
• Real Distribution Bias Correction — 4× in this paper
• Trace — 4× in this paper · explained in 2 episodes
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Area Under the Curve — 1× in this paper
• Area Under the Curve (AUC) — 1× in this paper · explained in 1 episode
• Central Limit Theorem — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• D Kernel Density Estimation — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sampling Biases — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark
• The DeepFake Detection Challenge (DFDC) Dataset

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