DIFC-Net: Diffusion-Intrinsic Feature Capture for AI-Generated Image Detection.

Shaofeng Lu, Jin Tian, Yujin Zhang, Fei Wu, Li Gong, Han Pan

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shaofeng LuSchool of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.ORCID 0009-0001-0492-4353

Jin TianSchool of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.ORCID 0009-0006-3133-6908

Yujin ZhangSchool of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.ORCID 0000-0003-1225-4334

Fei WuSchool of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.ORCID 0000-0003-4452-1687

Li GongSchool of Geographic Sciences, East China Normal University, Shanghai 200241, China.ORCID 0009-0007-9453-4159

Han PanSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0003-1574-3213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffusion models (e.g., Stable Diffusion, DALL·E 3) can now generate images that are nearly indistinguishable from real ones, making synthetic image detection increasingly challenging. We propose DIFC-Net, a diffusion-intrinsic detection framework that identifies AI-generated images by analyzing their reconstruction behavior during diffusion inversion rather than relying on visual artifacts. DIFC-Net jointly captures spatial discrepancy signals and latent diffusion trajectory evolution, and adaptively fuses them into a unified forensic representation. Extensive cross-model evaluations show that DIFC-Net achieves 90.29% average AUC on multiple unseen diffusion generators, outperforming state-of-the-art detectors while maintaining strong generalization without relying on training-time knowledge of specific generative models.

Indexed as

diffusion model detectionimage forensic analysislatent diffusion inversionmultimodal feature fusionresidual discrepancysynthetic image authenticity

Identifiers

PMID42076498

DOI10.3390/s26082389

PMCPMC13120258

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.