Real-time AI-Powered Deepfake & Synthetic Media Moderation with Contextual AI
This challenge focuses on building a robust, real-time moderation system capable of detecting and flagging potentially harmful AI-generated content. You will design an agentic workflow that integrates specialized detection models, an LLM for contextual analysis, and an inference platform for low-latency processing. The system must not only identify synthetic media but also provide actionable insights for human moderators, ensuring compliance with evolving content policies.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building a robust, real-time moderation system capable of detecting and flagging potentially harmful AI-generated content. You will design an agentic workflow that integrates specialized detection models, an LLM for contextual analysis, and an inference platform for low-latency processing. The system must not only identify synthetic media but also provide actionable insights for human moderators, ensuring compliance with evolving content policies.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master Contextual AI for building adaptive, stateful agent workflows that manage content streams and moderation decisions.
Implement real-time deepfake and synthetic media detection using open-source models (e.g., DeepFakeDetection by Facebook, or similar advanced architectures) deployed via Novita AI.
Integrate o4-mini for rapid classification of flagged content, generating summary reports, and drafting explanations for moderation actions.
Design a feedback loop system, potentially using Ax (Adaptive Experimentation), to refine detection models and moderation policies based on human review.
Build a scalable inference pipeline leveraging Novita AI to serve multiple detection models and the o4-mini LLM with high throughput and low latency.
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