Improving DeepFake Detection: A Comprehensive Review of Adversarial Robustness, Real-Time Processing and Evaluation Metrics

Authors

  • Najaf Saeed Faculty of Computer Science and Information Technology, Superior University, Lahore, 54000, Pakistan.
  • Gohar Mumtaz Faculty of Computer Science and Information Technology, Superior University, Lahore, 54000, Pakistan.
  • Muqaddas Yaqub Faculty of Computer Science and Information Technology, Superior University, Lahore, 54000, Pakistan.
  • Muhammad Haroon Ahmad Riphah International University, Lahore, 54000, Pakistan.

Keywords:

Deepfake Detection, Adversarial Robustness, Real-Time Processing

Abstract

This review analyzes 30 studies on deepfake detection. It focuses on three areas: adversarial robustness, real-time processing, and evaluation metrics. Deepfake technology is making rapid progress. It poses serious threats to digital security. We need strong, efficient detection models. The review exposes three key factors that boost detection system power. They are: adversarial training, GAN-based methods, and lightweight designs. They boost both resilience and efficiency. But challenges remain. We need real-time processing and standardized tests. They must capture the nuances of deepfake detection. The findings show a need for more research. It must address new threats, improve detection models, and set real-world benchmarks. The study stresses the need to improve deepfake detectors. We must integrate advanced training, optimize methods, and refine metrics. They must be robust, accurate, and adaptable to new digital threats.

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Published

2024-09-01

How to Cite

Najaf Saeed, Gohar Mumtaz, Muqaddas Yaqub, & Muhammad Haroon Ahmad. (2024). Improving DeepFake Detection: A Comprehensive Review of Adversarial Robustness, Real-Time Processing and Evaluation Metrics. Journal of Computing & Biomedical Informatics, 7(02). Retrieved from https://jcbi.org/index.php/Main/article/view/572