Sensing Deepfake Detection: A Survey of Detection Architectures, Adversarial Challenges, and Critical Applications in Political, Educational, and Military Domains

Deepfake technology has advanced swiftly, assisting the development of new technology trends such as the rapid production of hyper-realistic synthetic media that present considerable threats to digital security, privacy, military operations, and information integrity. This paper offers an extensive examination of visual intelligence and computer vision methodologies for deepfake detection, encompassing recent developments in deep learning, adversarial strategies, and feature extraction techniques. Specifically, it examines prevalent deepfake generation architectures, such as GANs, autoencoders, neural rendering, and diffusion models, in conjunction with novel adversarial strategies aimed at improving the realism of synthetic media while circumventing detection, particularly in military and intelligence-driven scenarios. Additionally, we investigate visual artifacts and manipulation traces, scrutinizing physical discrepancies, digital fingerprints, and physiological signals that function as critical indications for detection models. As such, the article offers a comprehensive analysis of CNN-based, transformer-based, and frequency-domain deepfake detection methodologies, highlighting their advantages, drawbacks, and practical relevance. Furthermore, we examine assessment measures, and the generalization difficulties encountered by detection methods and emphasize prospective research avenues, including explainable AI, self-supervised learning, and federated learning. This study is a significant resource for academics and practitioners combating deepfake disinformation in civilian, military, and hybrid threat environments, providing insights into detection improvements and impending issues in hostile AI.