Combating Digital Deception: A Comprehensive Framework for Deepfake Detection and Mitigation in the Era of Generative AI
Baldeo Prasad∗, Harsh Baliyan∗, Alan Alexander∗, and Mohd Farman Sajid∗
∗Department of Computer Science and Engineering Roorkee Institute of Technology
Roorkee, India baldeo.parsad@gmail.com, baliyanh625@gmail.com,
alexanderalan380@gmail.com, mohdfarman5545@gmail.com
Abstract—The fast development of generative artificial intel- ligence has popularized the development of hyper-realistic syn- thetic media, so-called deepfakes. What started as an intellectual interest has grown to be a major menace to information integrity, personal privacy and democratic procedures. Whereas early deepfakes could be identified with relative ease, the current state of the art systems based on Generative Adversarial Networks (GANs) and diffusion models create data that is difficult to differentiate by trained professionals. In this paper, a detailed overview of the deepfake scene is provided, both in terms of technology that facilitates the generation of synthetic media and countermeasures that are currently being developed to identify the latter. We discuss three main detection paradigms, namely artifact-based detectors, which detect technical inconsistencies in generated material, behavioral detectors, which detect unnatural occurrences in facial movements and speech, and blockchain- based provenance schemes, which build authentic media chains of custody. As we analyze, we find that despite the fact that there has been massive advancement in the detection capabilities, the arms race dynamic in the field is that of an underlying arms race as each step forward in detection increases the step forward in generation. We suggest a multi-layered defense system that entails the use of technical detection, online literacy schooling, platform policy, and legal deterrents. The framework notes that no single solution to the deepfake issue can be found in technology but a holistic approach that encompasses technical, educational, regulatory, and ethical aspects of the problem should be implemented in society. We prove that the deepfake problem is not only a technical issue but a philosophical ordeal of our capacity to continue to trust digital media in a time when seeing is no longer believing despite recent events, case studies, and trends that emerge.
Index Terms—Deepfakes, Synthetic Media Detection, Genera- tive Adversarial Networks, Digital Forensics, Media Authentica- tion, Misinformation, AI Ethics, Content Verification
