Image Generation and DeepFake Detection (Python Model)
The experience focused on training an AI model to generate realistic images and detect deepfakes using adversarial learning. The development required preparing and augmenting training data to improve robustness across input variations. The detection objective function was used to classify generated or manipulated media as deepfakes versus real images. • Designed and trained a GAN architecture (e.g., DCGAN and WGAN) to improve image quality and detection accuracy. • Implemented data augmentation techniques to strengthen the training dataset. • Collaborated to establish testing protocols and datasets for validating detection performance. • Drafted research content describing methods, findings, and implications in media integrity.