A Transfer-Learning Approach for Detection of Multiclass Synthetic Skin Cancer Images Generated by Deep Generative Models to Prevent Medical Insurance Fraud
Artificial Intelligence is advancing rapidly, raising critical concerns about the integrity of digital content, particularly in sensitive domains such as medical imaging. Recent AI techniques, such as Generative Adversarial Networks (GANs) and diffusion models, can generate highly realistic synthetic medical images, posing risks of misdiagnosis, inappropriate treatment, and other adverse outcomes.
