Back to search

Article

An Enhanced EfficientNetV2-L Framework for Detecting Deepfake Lung CT Images

2026-08-07

Abstract excerpt

<title>Abstract</title> <p>Authenticity in medical imaging is essential for accurate diagnosis and patient safety, yet recent advances in generative models have introduced risks of deepfake manipulation. This paper presents an enhanced EfficientNetV2-L framework for detecting deepfake lung CT images with improved accuracy and robustness. To strengthen the detection model, a balanced dataset of authentic and synth...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
50b89a68-2c3e-52db-9e00-c405e5ea682b
DOI
10.21203/rs.3.rs-6634989/v2
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
An Enhanced EfficientNetV2-L Framework for Detecting Deepfake Lung CT ImagesDOI 10.21203/rs.3.rs-6634989/v2
Select a neighboring publication to make it the new centre.