[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks (1909.07830v3)

Abstract: With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code and models are available at https://github.com/kamwoh/DeepIPR

• The paper proposes embedding unique digital passports in DNNs that cause significant performance degradation when unauthorized credentials are used.
• It demonstrates that passport-protected models maintain high verification accuracy under ambiguity attacks, as shown on networks like AlexNet and ResNet.
• The methodology offers a robust alternative to traditional watermarking, paving the way for secure MLaaS and future integration with federated learning and blockchain.

Overview of Passport-Based Deep Neural Network Ownership Verification

The paper "Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks" addresses the intellectual property (IP) challenges posed by the rapid development and commercialization of deep neural networks (DNNs). It provides an innovative solution to the verification of ownership rights of DNNs, aiming to protect these valuable machine learning models from illegal reproduction or misappropriation.

The authors critically evaluate existing digital watermarking methods, identifying vulnerability to ambiguity attacks that question the legitimacy of claimed ownership by reverse-engineering or counterfeit watermark creation. They propose a passport-based approach to DNN ownership verification, which introduces significant performance penalties when models are utilized without legitimate authentication credentials, i.e., the correct digital "passports".

Methodology and Experimental Results

The highlighted contribution of this research lies in the introduction and development of passport layers within the DNN architecture. These layers embed unique identification features, ensuring that the DNN's predictive performance degrades significantly if unauthorized passports are used. The technology provides stronger resistance to various forms of alteration attacks, such as fine-tuning and model pruning, which normally defeat conventional watermark-based approaches.

The paper demonstrates the efficacy of passport-based schemes through several rigorous experiments conducted using established DNN models like AlexNet and ResNet on datasets such as CIFAR10, CIFAR100, and Caltech-101. The results reveal that passport-protected models maintain high accuracy in ownership verification, even when subjected to extensive ambiguity attacks, including reverse engineering and redundancy removal techniques.

Implications and Future Directions

This passport-embedding methodology offers profound implications for securing the proprietary rights of DNN models and ensuring their legal utilization in Machine Learning as a Service (MLaaS) frameworks. By modulating network behavior based on digital passports, the authors provide a robust mechanism for ownership claim that extends the model's utility and potential commercial applications while respecting the creator's IP rights.

Future developments could explore deeper integration of passport-based security with federated learning systems and blockchain technologies to enhance decentralized model deployment and verification. Furthermore, another interesting avenue lies in applying this method to other IP-sensitive AI areas, such as natural language processing and generative models, which handle proprietary datasets and outputs.

The passport-based model ownership verification proposed in this paper marks a significant advancement in the security landscape for AI, addressing the increasing need for robust intellectual property protection in a world that is increasingly driven by data and models. As the field progresses, continued enhancements in algorithms and regulatory frameworks will be crucial to fortifying AI deployments against evolving threats and ensuring ethical use in diverse sectors.