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

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.