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arXiv · 2503.13224

ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction

Tong Zhou·Shijin Duan·Gaowen Liu·Charles Fleming·Ramana Rao Kompella·Shaolei Ren·Xiaolin Xu
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Abstract

Pre-trained models are valuable intellectual property, capturing both domain-specific and domain-invariant features within their weight spaces. However, model extraction attacks threaten these assets by enabling unauthorized source-domain inference and facilitating cross-domain transfer via the exploitation of domain-invariant features. In this work, we introduce **ProDiF**, a novel framework that leverages targeted weight space manipulation to secure pre-trained models against extraction attacks. **ProDiF** quantifies the transferability of filters and perturbs the weights of critical filters in unsecured memory, while preserving actual critical weights in a Trusted Execution Environment (TEE) for authorized users. A bi-level optimization further ensures resilience against adaptive fine-tuning attacks. Experimental results show that **ProDiF** reduces source-domain accuracy to near-random levels and decreases cross-domain transferability by 74.65\%, providing robust protection for pre-trained models. This work offers comprehensive protection for pre-trained DNN models and highlights the potential of weight space manipulation as a novel approach to model security.

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Tong Zhou, Shijin Duan, Gaowen Liu, Charles Fleming, Ramana Rao Kompella, Shaolei Ren, Xiaolin Xu. 2025-03-17. ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction. https://arxiv.org/abs/2503.13224

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