TY  - JOUR
AU  - Gazis, Alexandros
AU  - Pappas, Stylianos
AU  - Vavouras, Theodoros
AU  - Ali, Asim
AU  - Mastorakis, Nikos E.
PY  - 2026
DA  - 2026/09/23
TI  - Sensing Deepfake Detection: A Survey of Detection Architectures, Adversarial Challenges, and Critical Applications in Political, Educational, and Military Domains
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 3
SP  - 176
EP  - 196
DO  - 10.62762/TSCC.2026.303040
UR  - https://www.icck.org/article/abs/TSCC.2026.303040
KW  - deepfake detection
KW  - visual sensing and signal analysis
KW  - multimodal feature extraction
KW  - generative adversarial networks
KW  - adversarial robustness
KW  - physiological signal-based detection
KW  - frequency-domain analysis
KW  - explainable AI
KW  - privacy-preserving detection
KW  - federated learning
AB  - 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.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Gazis2026Sensing,
  author = {Alexandros Gazis and Stylianos Pappas and Theodoros Vavouras and Asim Ali and Nikos E. Mastorakis},
  title = {Sensing Deepfake Detection: A Survey of Detection Architectures, Adversarial Challenges, and Critical Applications in Political, Educational, and Military Domains},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {3},
  pages = {176-196},
  doi = {10.62762/TSCC.2026.303040},
  url = {https://www.icck.org/article/abs/TSCC.2026.303040},
  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.},
  keywords = {deepfake detection, visual sensing and signal analysis, multimodal feature extraction, generative adversarial networks, adversarial robustness, physiological signal-based detection, frequency-domain analysis, explainable AI, privacy-preserving detection, federated learning},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}