Open-Source Model Watermarking: Technical Approaches and Robustness Against Removal

Олег Ивченко; Iryna Ivchenko

doi:10.5281/zenodo.21934737

August 14, 2026Open Access

Open-Source Model Watermarking: Technical Approaches and Robustness Against Removal

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Key Points

Evaluate technical methodologies for embedding digital watermarks into open-source machine learning models and assess their vulnerability to removal attacks.
Categorized existing technical mechanisms used to embed identifiable signatures into open-source neural network weights, architectures, and generated outputs.
Evaluated the resilience of watermarking strategies against evasion and tampering attacks, including model fine-tuning, pruning, and parameter perturbation.
Model watermarking techniques demonstrate variable resilience, with open access to weights increasing susceptibility to removal attacks.
Maintaining robust watermark detection while preserving downstream model utility remains a central engineering trade-off across open-source architectures.

Implication

Technical review analyzes open-source model watermarking methods and resistance against removal attacks, highlighting challenges in protecting artificial intelligence intellectual property.

Abstract

Research article: Open-Source Model Watermarking: Technical Approaches and Robustness Against Removal

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Cite This Study

Ивченко et al. (2026) studied this question.

synapsesocial.com/papers/6a817a33f2fb91fc834addf7 https://doi.org/10.5281/zenodo.21934737

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Open-Source Model Watermarking: Technical Approaches and Robustness Against Removal

Key Points

Implication

Abstract

Discussion

Cite This Study

Open-Source Model Watermarking: Technical Approaches and Robustness Against Removal

Key Points

Implication

Abstract

Discussion

Cite This Study

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