CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
arXiv:2602.20419v1 Announce Type: new Abstract: Machine Learning as a Service (MLaaS) has emerged as a widely adopted paradigm for providing access to deep neural network (DNN) models, enabling users to conveniently leverage these models through standardized APIs. However, such services are highly vulnerable to Model Extraction Attacks (MEAs), where an adversary repeatedly queries a target model to collect input-output pairs and uses them to train a surrogate model that closely replicates its functionality. While numerous defense strategies have been proposed, verifying the ownership of a suspicious model with strict theoretical guarantees remains a challenging task. To address this gap, we introduce CREDIT, a certified ownership verification against MEAs. Specifically, we employ mutual information to quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold. We
arXiv:2602.20419v1 Announce Type: new Abstract: Machine Learning as a Service (MLaaS) has emerged as a widely adopted paradigm for providing access to deep neural network (DNN) models, enabling users to conveniently leverage these models through standardized APIs. However, such services are highly vulnerable to Model Extraction Attacks (MEAs), where an adversary repeatedly queries a target model to collect input-output pairs and uses them to train a surrogate model that closely replicates its functionality. While numerous defense strategies have been proposed, verifying the ownership of a suspicious model with strict theoretical guarantees remains a challenging task. To address this gap, we introduce CREDIT, a certified ownership verification against MEAs. Specifically, we employ mutual information to quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold. We extensively evaluate our approach on several mainstream datasets across different domains and tasks, achieving state-of-the-art performance. Our implementation is publicly available at: https://github.com/LabRAI/CREDIT.
Executive Summary
This article presents CREDIT, a certified ownership verification framework designed to counter Model Extraction Attacks (MEAs) in Machine Learning as a Service (MLaaS) environments. CREDIT employs mutual information to quantify the similarity between deep neural network models, establishing a verification threshold and offering rigorous theoretical guarantees for ownership verification. Experimental evaluations across various datasets and tasks demonstrate state-of-the-art performance. The publicly available implementation at https://github.com/LabRAI/CREDIT facilitates adoption. CREDIT's robustness against MEAs and its adaptability across different domains make it a valuable addition to the existing defense strategies. However, its applicability to real-world scenarios, particularly in terms of computational efficiency and scalability, warrants further investigation.
Key Points
- ▸ CREDIT employs mutual information to quantify the similarity between DNN models
- ▸ A practical verification threshold is proposed, providing rigorous theoretical guarantees
- ▸ State-of-the-art performance achieved in experimental evaluations across various datasets and tasks
Merits
Robustness against MEAs
CREDIT's use of mutual information and verification threshold effectively counter MEAs, ensuring the authenticity of DNN models in MLaaS environments.
Adaptability across domains
CREDIT's approach can be applied to various domains and tasks, making it a versatile defense strategy against MEAs.
Demerits
Computational efficiency
The computational cost of implementing CREDIT, particularly for large-scale models and datasets, may be substantial, potentially limiting its practical adoption.
Scalability
The scalability of CREDIT in handling high volumes of user queries and model updates in MLaaS environments requires further investigation.
Expert Commentary
CREDIT's innovative approach to ownership verification offers a promising direction for addressing the pressing issue of MEAs in MLaaS environments. While its merits are substantial, the limitations in computational efficiency and scalability must be carefully addressed to ensure its practical adoption. Furthermore, the policy implications of CREDIT warrant careful consideration in the development of regulatory frameworks for MLaaS services.
Recommendations
- ✓ Future research should focus on optimizing the computational efficiency and scalability of CREDIT, enabling its widespread adoption in MLaaS environments.
- ✓ The policy community should engage with the MLaaS industry to develop guidelines and regulations that ensure the authenticity and integrity of DNN models, leveraging CREDIT's innovative approach as a foundation for ownership verification.
