The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI), is making traditional reactive detection methods increasingly insufficient. This survey synthesizes emerging research on the shift toward proactive detection of adversarial synthetic campaigns. We adopt a unified, lifecycle-based taxonomy that combines socio-technical lifecycle models of coordinated synthetic campaigns with computational methodologies for synthetic content cluster detection. By structuring the analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), we integrate research streams from machine learning, network science, multimodal forensics, and social science. To differentiate synthetic amplification from authentic baseline traffic, this paper surveys techniques for modeling the creation, seeding, and propagation of new attack vectors, including Coordinated Inauthentic Behavior (CIB), epidemiological modeling, Hawkes processes, graph-based learning, multimodal/deepfake detection, and benchmark datasets. The survey further compares proactive detection methods at different C5 stages using operational criteria such as signal granularity, data requirements, detection lead time, scalability, erroneous-alert risk, and deployment maturity. Finally, we address GenAI-driven challenges, including moving-target synthetic swarms, multimodal drift, benchmark limitations, and the need for standards-based provenance, and we outline a measurable research roadmap for anticipatory and resilient information ecosystems.