Scalable Detection of Adversarial Synthetic Slop and Coordinated Media Abuse: A LoRA-Enabled Multimodal Defense System June 2026

Online video platforms face an exponential challenge in detecting and mitigating the flood of AI-generated “slop” and synthetic spam perpetuated by coordinated malicious actors. This content is increasingly designed to exploit the limitations of traditional media forensics, often utilizing generative AI to produce unique, localized variations of harmful or low-quality material at scale. Traditional content-centric moderation fails against this coordinated, adversarial generation strategy. This paper presents a novel, scalable defense system designed for online video platforms (OVP) to identify and terminate clusters of coordinated accounts exhibiting a prevalence of adversarial synthetic content. The approach leverages a multifaceted architecture incorporating two core machine learning components: a robust Coordinated Bot-Net Detector (via Account Relatedness) and a Synthetic Pattern Classifier. Crucially, we introduce an advanced AI enhancement layer utilizing Large Language Models (LLMs), specialized via Low-Rank Adaptation (LoRA) and Automatic Prompt Optimization (APO), to achieve rapid, high-precision semantic understanding of emerging synthetic spam trends. Test data demonstrates the system’s significant impact, resulting in the successful termination of clusters at a high precision comprising channels of synthetic spam generators. Furthermore, the LLM-driven automation significantly improves operational efficiency, resulting in significant human review efficiency gains. This work details a critical system design that provides essential scalability and adversarial resilience against sophisticated generative attacks.

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