OSAMA TARIQ2025-12-202025-12-202025https://escholar.umt.edu.pk/handle/123456789/17358Technology is advancing so fast that it is harder to capture fake images, and the emergence of generative models has allowed the generation of realistic synthetic images, popularly referred to as deepfakes. Although this progress has opened innovative possibilities, its abuse in critical fields such as healthcare poses great dangers. In this study, the task is to identify deepfake medical images, namely with malignant skin cancer instances. A special dataset was created, which contained not only real dermoscopic images but also synthetic ones, created with Deep Convolutional GAN (DCGAN), Wasserstein GAN (WGAN), and Stable Diffusion. These false pictures look almost similar to the actual malignancies and thus classifying them becomes a problem. A number of deep learning models were tested in equal conditions; VGG16, MobileNetV2, EfficientNetV2B2, and Xception. Nevertheless, the suggested DenseNet121 model, with a modified classification head and trained through transfer learning, demonstrated the highest result on all assessment measures. The images were divided into four classes, 1,200 training, 400 validation and 400 testing images. The models were evaluated on basis of accuracy, precision, recall, F1-score and a confusion matrix. The using of DenseNet121 with a training accuracy of 0.99 in the context of four classes (real and synthetic images using GAN models) showed high generalization and robustness and had the precision of 0.93, recall of 0.92, and F1-score of 0.92. The main limitation of the study is limited computational resources, so it was hard to generate synthetic images, which restricted the volume and variety of the dataset The work is relevant in the area of deepfake detection in medicine and demonstrates the significance of resorting to heavy CNN architectures such as DenseNet121 when constructing safe and trustworthy AI-powered diagnostic pipelines.enThesis