AI Model Watermarking for Protecting Intellectual Property in Privacy-Sensitive Systems

Authors: Anshul Goel, Anil Kumar Pakina, Mangesh Pujari

DOI: https://doi.org/10.37082/IJIRMPS.v11.i2.232381

Short DOI: https://doi.org/g9f4rx

Country: India

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Abstract: As artificial intelligence (AI) systems increasingly underpin critical applications, the protection of intellectual property (IP) embedded in machine learning (ML) models has emerged as a key concern. Model watermarking has been proposed as a promising method to embed identifiable signatures into AI models, enabling the rightful owner to assert authorship and track unauthorized use (Adi et al., 2018; Uchida et al., 2017). These techniques can be broadly classified into black-box and white-box watermarking approaches.
However, the integration of watermarking into privacy-sensitive environments introduces a host of challenges. Sectors such as healthcare and finance demand strict adherence to privacy standards and regulatory compliance, which can be jeopardized by poorly designed watermarking schemes (Zhang et al., 2019). Embedding watermarks must not degrade model performance or compromise sensitive data, posing a dilemma between traceability and data confidentiality.
Recent advancements have attempted to address this tension. Privacy-preserving watermarking mechanisms incorporating differential privacy (Abadi et al., 2016) or federated learning (Bonawitz et al., 2019) are gaining traction, offering avenues for secure model ownership verification without violating privacy policies. Meanwhile, the threat landscape continues to evolve, with adversaries developing techniques to remove, modify, or counterfeit embedded watermarks (Guo and Potluri, 2021).
This article explores the current state of AI watermarking technologies with a focus on their application in privacy-sensitive systems. We review core methodologies, assess their implications for system performance and security, and discuss evolving adversarial threats. Furthermore, we explore legal and ethical considerations, advocating for the standardization of watermarking practices to ensure defensibility and public trust.

Keywords: AI Watermarking, Machine Learning IP Protection, Model Ownership, Privacy-Sensitive Systems, Intellectual Property, Black-Box Watermarking, White-Box Watermarking, Deep Learning, AI Security, Model Verification, Federated Learning, Differential Privacy, Homomorphic Encryption, Adversarial Attacks, Model Tampering, Copyright Protection, Data Privacy, AI Regulation, Secure AI, Ethical AI, Model Fingerprinting, Neural Networks, IP Theft, Deepfake Detection, Privacy-Preserving AI, Model Authentication, Digital Watermark, Cybersecurity, AI Governance, Watermark Robustness

Paper Id: 232381

Published On: 2023-04-04

Published In: Volume 11, Issue 2, March-April 2023

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