Machine Learning-Based Detection and Defense Techniques for AI-Generated Content in Cybersecurity

Abstract

 This essay systematically reviews the detection and defense technologies for AI-generated content in the context of network security scenarios. First, it outlines the representative methods and key features of supervised learning, self-supervised learning, and unsupervised learning in image and video forgery detection. Second, it summarizes the active protection methods, such as adversarial or discriminative noise injection and digital watermarking, as well as the passive detection mechanisms, including ManTra-Net and ObjectFormer, along with their performance and applicability. Third, it discusses the necessity and the latest progress of multimodal fusion and lightweight deployment in practical implementation. On this basis, the paper identifies the major challenges in real-world applications, including the faint traces of high-fidelity forgeries, insufficient cross-domain generalization, scarcity of annotations, and weak adversarial robustness. Finally, it proposes future research directions, such as a generalized detection framework, robust multimodal fusion, dynamic adversarial defense and self-supervised robust training, and a task-aware lightweight architecture at the edge, providing references for subsequent research and engineering practice.