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NSFF: Noise and semantic features fusion for AI-generated image detection.

Haoran Yang¹, Ruiqiang Ma¹, Gang Wang¹

¹College of Intelligent Science and Technology, Inner Mongolia University of Technology, Hohhot, 010080, Inner Mongolia, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 22, 2026

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This study introduces NSFF, a novel framework for detecting AI-generated images by analyzing noise artifacts and semantic information. NSFF effectively identifies synthetic images from unseen models, improving misinformation detection.

Area of Science:

Computer Vision
Artificial Intelligence
Digital Forensics

Background:

Advanced generative models create realistic AI-generated images, posing risks for misinformation spread.
Existing AI-generated image detection methods require large datasets and struggle with novel generative models.

Purpose of the Study:

To develop a general and robust detection framework for AI-generated images.
To improve the generalization capability of AI-generated image detectors to unseen models.

Main Methods:

Proposed NSFF (Noise and Semantic Fusion Framework) for AI-generated image detection.
Utilized noise artifacts in poorly textured regions and combined global/local semantic information.
Trained on a small dataset (2000 images) for supervised learning.

Keywords:

Artifacts Feature fusion Generated image detection Noise feature Semantic feature

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Main Results:

NSFF demonstrated effective generalization to images from previously unseen generative models.
Achieved an average accuracy of 80.82% on the test set.
Outperformed previous methods relying solely on texture and semantic features.

Conclusions:

NSFF offers a more robust and generalizable approach to detecting AI-generated images.
The framework effectively addresses limitations of previous detection methods, particularly with novel generative models.
This research contributes to combating the malicious use of AI-generated imagery.