A Comprehensive Review of Deepfake Detection Techniques: From CNN-Based Models to Explainable Multimodal LLM Frameworks

Abstract

With the progress of generative adversarial networks (GANs), diffusion models, and large-scale deep learning models, deepfake technology has developed rather quickly and becomes a significant challenge to digital integrity, cybersecurity, journalism, and forensic investigations. While conventional CNN-based deepfake detectors demonstrated high accuracy in detecting visual artifacts; it only fulfills the lack of robustness as well as cross domain generalization and interpretability. The review provides an in-depth survey of fifteen state-of-art methodologies including classical CNN models, temporal-spatial video recognition, transformer-based networks, explainable AI (XAI) models, and models that combine multimodal large language model (LLM) products. The taxonomy classifies the current techniques in terms of representational features, learning models, interpretability procedures and deployment capability. Particular attention is paid to explainable and human-friendly AI systems including Grad-CAM visualizations, captioning systems, and narrative generation modules. Some of the important issues include adversarial robustness, bias in the data sets, computational complexity, cross-dataset generalization, and computational limitations in the real world. Lastly, the release of new research directions in terms of creating reliable, scalable, and understandable deepfake detectors is described.