Deepfake Detection Using Artificial Intelligence: A Review of Generation Techniques, Visual and Audio Forensics, Multimodal Analysis, Benchmark Datasets, and Deployable Detection Systems

Deepfake detection sits at the intersection of computer vision, speech processing, machine learning, and digital security. This review organized the literature into eight themes, compared detection approaches and datasets, and illustrated their integration through a proposed web-based detection system. The analysis shows that visual, frequency-based, meta-learning, audio, and multimodal methods each solve part of the problem, but that generalization to unseen generators, robustness to compression and adversarial attack, and noise-robust audio analysis remain unresolved. Addressing these challenges, together with joint audio-visual benchmarks and lightweight deployment, will be essential for detection systems that the public can trust and use.

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