IP Protection in the Era of Visual Generative AI: A Survey

2026-08-12

AI Summary

This study addresses intellectual property risks in visual generative AI by proposing a two-dimensional taxonomy based on control logic and asset type, overcoming limitations of traditional reviews. Through an intention-oriented literature survey and alignment analysis of evaluation protocols, this work systematically reconstructs the landscape of protection methodologies. It establishes a principled and systematic overview of IP protection while identifying critical open challenges, including proactive defense mechanisms, standardized evaluation benchmarks, and robustness. Consequently, this research provides the academic community with a clear theoretical framework and definitive guidance for future investigations, thereby advancing the systematic understanding of safeguarding intellectual property within the rapidly evolving domain of visual generative models.

Abstract

The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and redistribution of protected data and model assets. To address these risks, a growing body of technical defenses has been proposed. However, existing surveys typically organize this literature by lifecycle stage or technical mechanism, which can obscure the protective intent of different methods. This survey presents a two-dimensional taxonomy for IP protection in visual generative models. The primary axis is a Control Logic View, which classifies methods into Information Exposure Control, Generative Behavior Constraint, and Attribution & Accountability according to the risk variable they regulate. The secondary axis distinguishes Data IP from Model IP as cross-cutting asset dimensions. Under this framework, we systematically review protection methods, align evaluation protocols with protection objectives, and discuss open challenges including proactive model-level safeguards, standardized evaluation, robustness against adaptive attacks, and explainable evidence. This survey aims to offer a principled, systematic, and easy-to-follow overview for both new and experienced researchers in visual generative AI IP protection.

Problem

• Intellectual Property Protection
• Visual Generative AI
• IP Risks
• Taxonomy

Innovation

• Two-dimensional Taxonomy
• Control Logic View
• Visual Generative AI
• IP Protection
• Evaluation Alignment