A groundbreaking study from Konyang University unveils a lightweight AI model capable of detecting manipulated retinal images with near-perfect accuracy—outperforming trained ophthalmologists. Using deep learning tools like CycleGAN and Res U-Net, the AI identifies synthetic fundus images and even localizes manipulations with Grad-CAM heatmaps. As digital image tampering rises in medical diagnostics, this innovation offers a vital line of defense for clinical integrity and patient safety.