AI-IoT Enabled Surveillance Security: DeepFake Detection and Person Re-Identification Strategies

Face Recognition serves as a biometric tool and technological approach for identifying individuals based on distinctive facial features and physiological characteristics such as interocular distance, nasal width, lip contours, and facial structure. Among various identification methods, it stands out for its efficacy. However, the emergence of deepfake technology poses a significant security threat to real-time surveillance networks. In response to this challenge, we propose an AI-IoT enabled Surveillance security system framework aimed at mitigating deepfake-related risks. This framework is designed for person identification by leveraging facial features and characteristics. Specifically, we employ a Reinforcement Learning-based Deep Q Network framework for person identification and deepfake detection. Through the integration of AI and IoT technologies, our framework offers enhanced surveillance security by accurately identifying individuals while effectively detecting and combating deepfake-generated content. This research contributes to the advancement of surveillance systems, providing a robust solution to address emerging security threats in real-time monitoring environments. The introduction of this Deep Q Network, is useful to build real-time surveillance framework where live images are identified by a continuous learning mechanism and solves the security issues by a feedback mechanism.

@article{Bethu2024,
  title     = {AI-IoT Enabled Surveillance Security: DeepFake Detection and Person Re-Identification Strategies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Srikanth Bethu and M. Trupthi and Suresh Kumar Mandala and Syed Karimunnisa and Ayesha Banu},
  doi       = {10.14569/IJACSA.2024.0150799},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150799}
}