Loss‐Attention Physics‐Informed Neural Networks for Enhanced Deepfake Detection in Medical Image Classification: A Diagnostic Performance Evaluation

Background and Purpose In medical imaging, identifying deepfakes is crucial to identifying altered or fake images, ensuring reliable and authentic medical images, and potentially preventing fraudulent or incorrect diagnoses. The potential for fraudulent changes that go unnoticed and high computational costs are obstacles. Methods To address this issue, MDD‐MI‐LAPINN was proposed for medical deepfake detection. Images from Knee Osteoarthritis and Lung CT Scan datasets were pre‐processed using the Implicit Unscented Particle Filter (IUPF), followed by classification through a Loss‐Attention Physics‐Informed Neural Network (LAPINN) to distinguish authentic and manipulated medical images. Results The new MDD‐MI‐LAPINN method was coded in Python and validated by typical performance metrics such as accuracy, precision, recall, and F 1‐score. The experimental results show that MDD‐MI‐LAPINN outperformed the following techniques: EMDD‐MI‐YOLO (Medical Deepfake Detection in Medical Images), MDD‐MedNet (Medical Deepfake Detection Using an Improved DL Technique), and DD‐AGAA‐CNN (Deepfake Detection: Examining Methods Generalization Across Frameworks). Accuracy was 99.34% for real images and 98.74% for fake images, in addition to high recall, precision, and F 1‐scores across all measures. These results identify MDD‐MI‐LAPINN’s superiority in consistently and accurately detecting and classifying medical deepfake images in real and fake images. Discussion MDD‐MI‐LAPINN achieves superior accuracy and robustness in detecting medical deepfakes, outperforming existing methods by integrating IUPF and LAPINN for precise and reliable medical image classification.