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

Abstract

Deepfake technology has advanced swiftly, enabling the rapid production of hyper-realistic synthetic media that pose considerable threats to digital security, privacy, military operations, and information integrity. This paper extensively examines visual intelligence and computer vision methodologies for deepfake detection, covering recent developments in deep learning, adversarial strategies, and feature extraction. It reviews prevalent generation architectures—including GANs, autoencoders, neural rendering, and diffusion models—alongside novel adversarial tactics that enhance realism while evading detection, particularly in military and intelligence contexts. We also investigate visual artifacts and manipulation traces, scrutinizing physical discrepancies, digital fingerprints, and physiological signals as critical detection indicators. The article offers a comprehensive analysis of CNN-based, transformer-based, and frequency-domain detection methods, highlighting their advantages, drawbacks, and practical relevance. Furthermore, we examine assessment measures and generalization challenges, while emphasizing prospective research avenues such as explainable AI, self-supervised learning, and federated learning. This study serves as 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.