implementation

OpenAI Multimodal Integration for Analysis

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Building Adversarial Deepfake Detection

Format

Text-first

Lines

Sections

Linked challenge

Building Adversarial Deepfake Detection

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Implement the 'Image Analyzer' agent to utilize OpenAI 'Next-Gen Multimodal' capabilities. Detail how this agent will process input images and associated prompts to detect visual inconsistencies, deepfake artifacts, and analyze the user's intent. Design prompt structures that elicit comprehensive analysis from the multimodal model, focusing on robustness against subtle manipulations.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked 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.

Open challenge

Related prompts

Browse library

Agent Roles and AutoGen Configuration

planning

A2A Protocol for Red Teaming and Policy Sharing

implementation

Adversarial Testing and Policy Refinement

testing