FIND: A Simple yet Effective Baseline for Diffusion-Generated Image Detection

2026-03-15

AI Summary

Existing methods for detecting images generated by diffusion models rely on time-consuming reconstruction and exhibit poor generalization. This work proposes FIND, a novel approach that, for the first time, trains a lightweight binary classifier by perturbing real images with Gaussian noise and labeling them as synthetic samples. FIND directly captures the intrinsic distributional discrepancy between real and synthetic images in terms of their difficulty in Gaussian fitting, without requiring image reconstruction or priors specific to any generative model. The method establishes an end-to-end efficient detection framework that achieves strong performance on the GenImage benchmark, improving detection accuracy by 11.7% while operating 126 times faster than current state-of-the-art approaches—demonstrating a significant advance in balancing universality, efficiency, and practicality.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The remarkable realism of images generated by diffusion models poses critical detection challenges. Current methods utilize reconstruction error as a discriminative feature, exploiting the observation that real images exhibit higher reconstruction errors when processed through diffusion models. However, these approaches require costly reconstruction computations and depend on specific diffusion models, making their performance highly model-dependent. We identify a fundamental difference: real images are more difficult to fit with Gaussian distributions compared to synthetic ones. In this paper, we propose Forgery Identification via Noise Disturbance (FIND), a novel method that requires only a simple binary classifier. It eliminates reconstruction by directly targeting the core distributional difference between real and synthetic images. Our key operation is to add Gaussian noise to real images during training and label these noisy versions as synthetic. This step allows the classifier to focus on the statistical patterns that distinguish real from synthetic images. We theoretically prove that the noise-augmented real images resemble diffusion-generated images in their ease of Gaussian fitting. Furthermore, simply by adding noise, they still retain visual similarity to the original images, highlighting the most discriminative distribution-related features. The proposed FIND improves performance by 11.7% on the GenImage benchmark while running 126x faster than existing methods. By removing the need for auxiliary diffusion models and reconstruction, it offers a practical, efficient, and generalizable way to detect diffusion-generated content.

Problem

• diffusion-generated image detection
• image forgery detection
• real vs. synthetic images
• distributional difference

Innovation

• diffusion-generated image detection
• Gaussian noise augmentation
• distributional difference
• reconstruction-free
• binary classification