Towards Generalizable Deepfake Detection via Real Distribution Bias Correction

• URL: http://arxiv.org/abs/2603.14005v1
• Date: Sat, 14 Mar 2026 16:11:00 GMT
• Title: Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
• Abstract summary: We introduce the Real Distribution Bias Correction (RDBC) framework, which consists of two key components.<n>RDBC achieves state-of-the-art performance in both in-domain and cross-domain deepfake detection.
• Score: 62.89674593681203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using available source domain data. However, predicting an unbounded set of future manipulations from limited prior examples is infeasible. To overcome this limitation, we propose to exploit the invariance of \textbf{real data} from two complementary perspectives: the fixed population distribution of the entire real class and the inherent Gaussianity of individual real images. Building on these properties, we introduce the Real Distribution Bias Correction (RDBC) framework, which consists of two key components: the Real Population Distribution Estimation module and the Distribution-Sampled Feature Whitening module. The former utilizes the independent and identically distributed (\iid) property of real samples to derive the normal distribution form of their statistics, from which the distribution parameters can be estimated using limited source domain data. Based on the learned population distribution, the latter utilizes the inherent Gaussianity of real data as a discriminative prior and performs a sampling-based whitening operation to amplify the Gaussianity gap between real and fake samples. Through synergistic coupling of the two modules, our model captures the real-world properties of real samples, thereby enhancing its generalizability to unseen target domains. Extensive experiments demonstrate that RDBC achieves state-of-the-art performance in both in-domain and cross-domain deepfake detection.

Related papers

• On the Predictive Power of Representation Dispersion in Language Models [27.85164935773766]
  We show that models that spread contextual representations more widely tend to achieve lower perplexity.<n>We show how dispersion can be leveraged for a range of practical tasks without requiring labeled data.
  arXiv  Detail & Related papers  (2025-06-30T17:53:50Z)
• Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers [49.97755400231656]
  We present the first performance guarantee with explicit dimensional dependencies for general score-mismatched diffusion samplers.<n>We show that score mismatches result in an distributional bias between the target and sampling distributions, proportional to the accumulated mismatch between the target and training distributions.<n>This result can be directly applied to zero-shot conditional samplers for any conditional model, irrespective of measurement noise.
  arXiv  Detail & Related papers  (2024-10-17T16:42:12Z)
• Improving Distribution Alignment with Diversity-based Sampling [0.0]
  Domain shifts are ubiquitous in machine learning, and can substantially degrade a model's performance when deployed to real-world data. This paper proposes to improve these estimates by inducing diversity in each sampled minibatch. It simultaneously balances the data and reduces the variance of the gradients, thereby enhancing the model's generalisation ability.
  arXiv  Detail & Related papers  (2024-10-05T17:26:03Z)
• Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks [0.6906005491572401]
  We propose a method to ensure that the distributions of certain generated data statistics coincide with the respective distributions of the real data. We evaluate the method on a synthetic dataset and a real-world dataset and demonstrate improved performance of our approach.
  arXiv  Detail & Related papers  (2023-06-19T14:03:27Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Estimating Generalization under Distribution Shifts via Domain-Invariant Representations [75.74928159249225]
  We use a set of domain-invariant predictors as a proxy for the unknown, true target labels. The error of the resulting risk estimate depends on the target risk of the proxy model.
  arXiv  Detail & Related papers  (2020-07-06T17:21:24Z)
• Global Distance-distributions Separation for Unsupervised Person Re-identification [93.39253443415392]
  Existing unsupervised ReID approaches often fail in correctly identifying the positive samples and negative samples through the distance-based matching/ranking. We introduce a global distance-distributions separation constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view. We show that our method leads to significant improvement over the baselines and achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-01T07:05:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.