Title: Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

Abstract:

The rapid expansion of adversarial synthetic material, fueled by Generative AI (GenAI), has rendered conventional reactive detection strategies obsolete. This survey consolidates contemporary research to illustrate a fundamental transition toward the proactive identification of nascent inauthentic narratives. We employ a cohesive, lifecycle-oriented taxonomy that merges socio-technical frameworks of adversarial campaigns with sophisticated computational approaches for detecting emerging inauthenticity.

By organizing our analysis through the C5 Interaction Model—which encompasses Context, Causes, Content, Cycle of Amplification, and Consequences—we bridge distinct research domains from both social science and machine learning. To distinguish the dissemination patterns of synthetic amplification from genuine baseline traffic, this paper reviews cutting-edge techniques for modeling the genesis, seeding, and spread of new narratives. Key methodologies examined include the analysis of Coordinated Inauthentic Behavior (CIB), epidemiological modeling, and Hawkes processes.

Furthermore, this work offers a systematic evaluation of proactive detection mechanisms for adversarial threats across various stages of the C5 model. These include anomaly detection within high-dimensional embedding spaces, unsupervised coordination identification on multi-layer graphs, and the application of agentic AI systems. Finally, the survey confronts the specific challenges introduced by GenAI, such as the complexity of monitoring swiftly evolving threats and managing multi-level distributional drift. It concludes by proposing a future research agenda centered on identifying anomalous clusters and developing anticipatory, resilient systems. Ultimately, this survey delivers a thorough, lifecycle-based assessment of methods designed to proactively detect emerging synthetic threats, thereby fostering more robust information ecosystems.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC