Detecting Deepfakes: Training Adversarial Detectors with GANs for Image Authentication
Vijay Kiran R, R Manish, Trupthi Rao, Ashwini Kodipalli, N Gargi, Bhavani Soma · 2024
The credibility of visual content is under serious threat from deepfake. In this research, we introduce a method that mixes face-swap techniques and Generative Adversarial Networks (GANs) to enhance the capacity for detecting deepfakes. Our approach involves capturing an individual’s facial attributes and applying these to other individuals’ faces to create generated images that look real. These produced pictures become powerful tools for detecting deepfakes in comparison with traditional methods. We make varying datasets features generated images so as to train our deepfake detectors. During this process, training data sets are used by the detectors to enable them adapt with the progressing deepfake creating arena. An experiment compared our strategy with other methods on how it improved accuracy of the identification of videos in different scenarios/contexts relevant to discussions about race or ethnicity in American society today - showing that ours was superior across all settings tested (p<0). The system can better recognize objects when manipulated through more advanced techniques like face swapping if GANs are integrated within it than without them. This research represents an important step forward in the development of detection systems in the face of deepfake technologies increasing complexity, so as to mitigate some possible risks associated with altered media materials’ dissemination.