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Multi-Agent Framework for Controllable and Protected Generative Content Creation: Addressing Copyright and Provenance in AI-Generated Media

Haris Khan, Sadia Asif, Shumaila Asif

TL;DR

This work tackles controllability and content provenance in AI-generated media by introducing a multi-agent framework with Director, Generator, Reviewer, Integration, and Protection agents and integrated watermarking. The approach decomposes complex creative tasks into controllable subtasks while embedding provenance markers throughout generation, enabling user-guided refinement and ownership verification. Case studies on creative content generation and commercial copyright protection, along with feasibility metrics (e.g., up to 23% alignment gains and >90% watermark recovery), demonstrate practical viability. The framework advances responsible AI deployment by supporting verifiable provenance, brand-consistent outputs, and legal compliance in professional workflows.

Abstract

The proliferation of generative AI systems creates unprecedented opportunities for content creation while raising critical concerns about controllability, copyright infringement, and content provenance. Current generative models operate as "black boxes" with limited user control and lack built-in mechanisms to protect intellectual property or trace content origin. We propose a novel multi-agent framework that addresses these challenges through specialized agent roles and integrated watermarking. Our system orchestrates Director, Generator, Reviewer, Integration, and Protection agents to ensure user intent alignment while embedding digital provenance markers. We demonstrate feasibility through two case studies: creative content generation with iterative refinement and copyright protection for AI-generated art in commercial contexts. Preliminary feasibility evidence from prior work indicates up to 23\% improvement in semantic alignment and 95\% watermark recovery rates. This work contributes to responsible generative AI deployment, positioning multi-agent systems as a solution for trustworthy creative workflows in legal and commercial applications.

Multi-Agent Framework for Controllable and Protected Generative Content Creation: Addressing Copyright and Provenance in AI-Generated Media

TL;DR

This work tackles controllability and content provenance in AI-generated media by introducing a multi-agent framework with Director, Generator, Reviewer, Integration, and Protection agents and integrated watermarking. The approach decomposes complex creative tasks into controllable subtasks while embedding provenance markers throughout generation, enabling user-guided refinement and ownership verification. Case studies on creative content generation and commercial copyright protection, along with feasibility metrics (e.g., up to 23% alignment gains and >90% watermark recovery), demonstrate practical viability. The framework advances responsible AI deployment by supporting verifiable provenance, brand-consistent outputs, and legal compliance in professional workflows.

Abstract

The proliferation of generative AI systems creates unprecedented opportunities for content creation while raising critical concerns about controllability, copyright infringement, and content provenance. Current generative models operate as "black boxes" with limited user control and lack built-in mechanisms to protect intellectual property or trace content origin. We propose a novel multi-agent framework that addresses these challenges through specialized agent roles and integrated watermarking. Our system orchestrates Director, Generator, Reviewer, Integration, and Protection agents to ensure user intent alignment while embedding digital provenance markers. We demonstrate feasibility through two case studies: creative content generation with iterative refinement and copyright protection for AI-generated art in commercial contexts. Preliminary feasibility evidence from prior work indicates up to 23\% improvement in semantic alignment and 95\% watermark recovery rates. This work contributes to responsible generative AI deployment, positioning multi-agent systems as a solution for trustworthy creative workflows in legal and commercial applications.
Paper Structure (18 sections, 2 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-Agent Generative Systems
  4. Controllability and User Alignment
  5. Content Protection and Provenance
  6. Proposed Multi-Agent Framework
  7. Architecture Overview
  8. Agent Roles and Responsibilities
  9. Interactive Control Mechanism
  10. Algorithmic Workflow
  11. Case Studies and Preliminary Feasibility
  12. Case Study 1: Creative Content Generation
  13. Case Study 2: Copyright Protection in Commercial Applications
  14. Implementation and Feasibility Evidence
  15. Discussion and Conclusion
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Multi-agent framework for controllable and protected generative content creation. The pipeline consists of specialized agents for planning, generation, review, integration, and protection, with iterative human-in-the-loop feedback and provenance logging.
  • Figure 2: Illustrative case study showing the multi-agent pipeline processing the prompt "Red dragon flying over a castle at sunset." The framework decomposes, generates, reviews, integrates, and protects content with embedded watermarking.