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.
Develop an agentic system using Contextual AI to orchestrate real-time deepfake detection and moderation workflows, leveraging specialized models and fast inference.
How work is evaluated
The solution will be evaluated on its ability to accurately detect deepfakes, provide relevant moderation explanations, and process content streams efficiently. Performance metrics will include detection accuracy, explanation quality, and inference latency.
Challenge datasets
Public datasets, plus the private datasets you have access to.
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.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The solution will be evaluated on its ability to accurately detect deepfakes, provide relevant moderation explanations, and process content streams efficiently. Performance metrics will include detection accuracy, exp...
Challenge input
{'content_id': 'string', 'media_url': 'string', 'metadata': {'timestamp': 'datetime'}}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'content_id': 'string', 'is_deepfake': 'boolean', 'flagged_category': 'string', 'explanation': 'string', 'confidence_score': 'float'}
- Verify successful integration and communication between Contextual AI agents, detection models, and Novita AI.
- Check if generated explanations adhere to specified moderation policy guidelines.
- Detection Accuracy target: 0.9
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- Verify successful integration and communication between Contextual AI agents, detection models, and Novita AI.
- Check if generated explanations adhere to specified moderation policy guidelines.
Metrics
- Detection Accuracy · Target 0.9
- Explanation Coherence · Target 0.8
- Inference Latency (ms) · Target 150
Workflow tasks
- Task 1
Input: {'content_id': 'string', 'media_url': 'string', 'metadata': {'timestamp': 'datetime'}}
Output: {'content_id': 'string', 'is_deepfake': 'boolean', 'flagged_category': 'string', 'explanation': 'string', 'confidence_score': 'float'}
Execution harness: Python
Recipe freeze marker
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