Challenge

Building Adversarial Deepfake Detection

Following reports of advanced AI models generating non-consensual deepfakes, this challenge focuses on developing an ethical AI solution for robust deepfake detection and content moderation. You will build a multi-agent system using AutoGen, where agents collaborate and debate to identify and flag AI-generated visual content that violates ethical guidelines. The system will integrate OpenAI 'Next-Gen Multimodal' capabilities for nuanced image and text analysis, enabling the agents to understand context and detect subtle manipulations. A2A Protocol will facilitate secure and structured communication between 'Moderation' agents and 'Red Team' agents, with the latter attempting to bypass the moderation system with adversarial prompts, pushing the boundaries of ethical AI robustness. Extended thinking and dynamic policy RAG will ensure comprehensive and adaptable moderation.

AI DevelopmentHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Following reports of advanced AI models generating non-consensual deepfakes, this challenge focuses on developing an ethical AI solution for robust deepfake detection and content moderation. You will build a multi-agent system using AutoGen, where agents collaborate and debate to identify and flag AI-generated visual content that violates ethical guidelines. The system will integrate OpenAI 'Next-Gen Multimodal' capabilities for nuanced image and text analysis, enabling the agents to understand context and detect subtle manipulations. A2A Protocol will facilitate secure and structured communication between 'Moderation' agents and 'Red Team' agents, with the latter attempting to bypass the moderation system with adversarial prompts, pushing the boundaries of ethical AI robustness. Extended thinking and dynamic policy RAG will ensure comprehensive and adaptable moderation.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Loading datasets...
Learning goals

What you should walk away with

  • Master AutoGen for orchestrating multi-agent conversations, negotiation, and consensus-building among 'Moderation' and 'Policy' agents.

  • Implement advanced multimodal analysis using OpenAI 'Next-Gen Multimodal' to detect visual inconsistencies and analyze textual prompts for malicious intent.

  • Design and deploy A2A Protocol for secure, cross-platform communication, enabling 'Red Team' agents to share adversarial techniques and 'Moderation' agents to update their defenses.

  • Build extended thinking pipelines for ethical reasoning, allowing agents to deliberate on moderation decisions based on nuanced policy interpretations and potential harm.

  • Integrate RAG (Retrieval Augmented Generation) for dynamic policy lookup, enabling agents to adapt to evolving ethical guidelines and content standards.

  • Develop prompt hardening techniques and adversarial training strategies to improve the system's resilience against jailbreaking and deepfake generation attempts.

Start from your terminal
$npx -y @versalist/cli start building-adversarial-deepfake-detection

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

Docs
Manage API keys
Host and timing
Vera

AI Research & Mentorship

Starts Available now
Evergreen challenge
Your progress

Participation status

You haven't started this challenge yet

Timeline and host

Operating window

Key dates and the organization behind this challenge.

Start date
Available now
Run mode
Evergreen challenge
Explore

Find another challenge

Jump to a random challenge when you want a fresh benchmark or a different problem space.

Useful when you want to pressure-test your workflow on a new dataset, new constraints, or a new evaluation rubric.

Tool Space Recipe

Draft
Evaluation

Frequently Asked Questions about Building Adversarial Deepfake Detection