From Deep Learning to Large Models: Detection Methods for AI-Generated Content and Deepfakes

The rapid evolution from traditional deep learning to large-scale foundation models has revolutionized content gen- eration while introducing unprecedented challenges for detecting AI-generated content (AIGC) and deepfakes. This paper comprehensively analyzes the detection technologies across the paradigm shift from GANs to modern diffusion models and large language models (LLMs). We systematically categorize detection methods across generation eras, modalities (text, image, video, audio), and technical approaches. Our analysis reveals that traditional methods designed for GAN-generated content exhibit catastrophic failure on modern diffusion and LLM outputs. We examine state- of-the-art techniques such as watermarking schemes, zero- shot detection methods like DetectGPT, reconstruction-based ap- proaches (DIRE), and multimodal verification. Furthermore, we analyze critical challenges related to cross-model generalization, adversarial robustness, and computational efficiency, providing practical guidance for method selection and deployment.