Multi-Agent System for Adversarial Robustness and Originality Attribution in Short-Form Videos

The explosive proliferation of Short-Form Video (SFV) has created a high-velocity ecosystem that defies traditional analysis. Recommendation and discovery systems now face two critical adversarial behaviors at scale: Modality Misalignment, where creators insert distinctive text, images, or hidden audio/video clips with hidden agendas to manipulate algorithmic distribution, and Content Theft, where viral content is slightly tweaked and reposted as original to capture monetization. While monolithic Multi-Modal Large Language Models (MLLMs) offer strong zero-shot capabilities, they are increasingly insufficient for this production reality; their high computational cost, tendency for Object and Cross-Modal Hallucination, and lack of external context make them ill-suited for detecting fine-grained manipulation.