OWASP AI Model Watermarking

The AI industry has experienced exponential growth, with the global AI market projected to reach $1.81 trillion by 2030, growing at a CAGR of approximately 36.6%. This rapid advancement has led to an unprecedented surge in AI model development and deployment, resulting in challenges related to intellectual property protection, attribution, unauthorized use, and regulatory compliance.

Key Challenges:

Model Theft: Training large AI models costs $200K–$1.2M, yet IP theft exceeds $10B annually. Model extraction attacks can replicate proprietary models with 98% fidelity.

Attribution Issues: With 100K+ public AI models, 67% of companies modify pre-trained models, but only 12% have robust tracking systems.

Unauthorized Deployments: 58% of enterprises lack deployment tracking, leading to 3-4 unauthorized copies per company.

Regulatory Compliance: AI laws in 30+ countries mandate provenance tracking, yet 72% of organizations struggle with audits.

To address these challenges, a robust and verifiable watermarking solution is essential. The AI Model Watermarking will be an open-source initiative aimed at developing a comprehensive solution for embedding and detecting watermarks in AI and ML models. This application will enable individuals and organizations to protect their intellectual property and verify the authenticity of models in deployment. With proper watermarking, organizations can reduce unauthorized usage by up to 85% and cut compliance costs by 60%.

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Key Objectives

• Develop zero-knowledge proof-based watermarking techniques for various types of AI/ML models.
• Research and implement ZK-based ownership verification protocols.
• Create an open-source application that’s accessible by all and extensible based on the need.
• Establish methods for watermark verification and extraction.
• Conduct research on watermark resilience against various attack.
• Build a proof-of-concept that can be evolved into a production-ready solution.

Road Map

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Phase 1: Research and Planning (4-5 months)

• Month 1:
• Form research team and establish collaboration framework

• Conduct comprehensive literature review

• Month 2-3 :
• Research and evaluate existing watermarking techniques
• Document findings and recommendations

• Begin exploration of ZK proof systems

• Month 4:
• Research ZK-specific requirements for AI model watermarking
• Analyze trade-offs between different ZK proof systems

• Define initial technical requirements

• Month 5:
• Develop proof-of-concept ZK circuits for watermark verification
• Document findings and recommendations

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Phase 2: Core Development (4-5 months)

• Month 6-7:
• Design core architecture
• Implement basic watermarking functionality

• Develop plugin system

• Month 8-9:
• Create integration interfaces
• Build test infrastructure
• Implement initial set of watermarking techniques

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Phase 3: Testing and Validation (3-4 months)

• Month 9-10:
• Develop validation framework
• Implement attack simulations

• Conduct security analysis

• Month 11:
• Performance testing and optimization
• Documentation and examples
• Community feedback collection

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Phase 4: Product Development (3-4 months)

• Month 12-14:
• Refine framework based on testing results
• Create comprehensive documentation

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Ongoing Activities

• Regular security assessments
• Community engagement and contribution management
• Documentation updates
• Research paper publications
• Integration with new ML frameworks as they emerge

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Success Criteria

• Framework successfully watermarks models with minimal impact on performance
• Watermarks survive common model transformation attacks
• Open-source community adoption and contribution
• Comprehensive documentation and examples
• Positive security assessment results
• Successful integration with major ML frameworks