RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection

• URL: http://arxiv.org/abs/2601.18900v1
• Date: Mon, 26 Jan 2026 19:11:31 GMT
• Title: RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection
• Abstract summary: We introduce a rigorous, statistically grounded framework for fake image detection that focuses on producing a probability score interpretable with respect to the real-image population.<n>This framework is generic, flexible, and training-free, making it well-suited for robust fake image detection across diverse and evolving settings.
• Score: 3.4104142774745996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As generative models continue to evolve, detecting AI-generated images remains a critical challenge. While effective detection methods exist, they often lack formal interpretability and may rely on implicit assumptions about fake content, potentially limiting robustness to distributional shifts. In this work, we introduce a rigorous, statistically grounded framework for fake image detection that focuses on producing a probability score interpretable with respect to the real-image population. Our method leverages the strengths of multiple existing detectors by combining training-free statistics. We compute p-values over a range of test statistics and aggregate them using classical statistical ensembling to assess alignment with the unified real-image distribution. This framework is generic, flexible, and training-free, making it well-suited for robust fake image detection across diverse and evolving settings.

Related papers

• SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images [19.262732315838107]
  We propose SimLBR, a simple and efficient framework for fake image detection using Latent Blending Regularization (LBR)<n>Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark.<n>All code and models will be released on HuggingFace and GitHub.
  arXiv  Detail & Related papers  (2026-02-23T23:22:41Z)
• PDA: Generalizable Detection of AI-Generated Images via Post-hoc Distribution Alignment [16.98090845687867]
  Post-hoc Distribution Alignment (PDA) is a novel approach for the generalizable detection for AI-generated images.<n>Our work provides a flexible and effective solution for real-world fake image detection, advancing the generalization ability of detection systems.
  arXiv  Detail & Related papers  (2025-02-15T13:55:34Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Epistemic Uncertainty for Generated Image Detection [107.62647907393377]
  We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models.<n>Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models.
  arXiv  Detail & Related papers  (2024-12-08T11:32:25Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Can Your Generative Model Detect Out-of-Distribution Covariate Shift? [2.0144831048903566]
  We propose a novel method for detecting Out-of-Distribution (OOD) sensory data using conditional Normalizing Flows (cNFs) Our results on CIFAR10 vs. CIFAR10-C and ImageNet200 vs. ImageNet200-C demonstrate the effectiveness of the method.
  arXiv  Detail & Related papers  (2024-09-04T19:27:56Z)
• On quantifying and improving realism of images generated with diffusion [50.37578424163951]
  We propose a metric, called Image Realism Score (IRS), computed from five statistical measures of a given image. IRS is easily usable as a measure to classify a given image as real or fake. We experimentally establish the model- and data-agnostic nature of the proposed IRS by successfully detecting fake images generated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN. Our efforts have also led to Gen-100 dataset, which provides 1,000 samples for 100 classes generated by four high-quality models.
  arXiv  Detail & Related papers  (2023-09-26T08:32:55Z)
• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging [88.20869695803631]
  We show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value. We evaluate our procedure on three image-to-image regression tasks.
  arXiv  Detail & Related papers  (2022-02-10T18:59:56Z)
• Robustness via Uncertainty-aware Cycle Consistency [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-10-24T15:33:21Z)
• Uncertainty-aware Generalized Adaptive CycleGAN [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner. Existing methods often learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method called Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-02-23T15:22:35Z)

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.