Protecting Intellectual Property With Reliable Availability of Learning Models in AI-Based Cybersecurity Services
Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications have raised the need for intellectual property protection of using deep neural network (DNN). Existing studies (e.g., watermarking techniques) on intellectual property protection only aim at inserting secret information into DNNs, allowing producers to detect whether the given DNN infringes on their own copyrights. However, since the availability protection of learning models is rarely considered, the piracy model can still work with high accuracy. In this paper, a novel model locking (M-LOCK) scheme for the DNN is proposed to enhance its availability protection, where the DNN produces poor accuracy if a specific token is absent, while it maps only the tokenized inputs into correct predictions. The proposed scheme performs the verification process during the DNN inferenc
Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications have raised the need for intellectual property protection of using deep neural network (DNN). Existing studies (e.g., watermarking techniques) on intellectual property protection only aim at inserting secret information into DNNs, allowing producers to detect whether the given DNN infringes on their own copyrights. However, since the availability protection of learning models is rarely considered, the piracy model can still work with high accuracy. In this paper, a novel model locking (M-LOCK) scheme for the DNN is proposed to enhance its availability protection, where the DNN produces poor accuracy if a specific token is absent, while it maps only the tokenized inputs into correct predictions. The proposed scheme performs the verification process during the DNN inference operation, actively protecting models' intellectual property copyright at each query. Specifically, to train the token-sensitive decision-making boundaries of DNNs, a data poisoning-based model manipulation (DPMM) method is also proposed, which minimizes the correlation between the dummy outputs and correct predictions. Extensive experiments demonstrate the proposed scheme could achieve high reliability and effectiveness across various benchmark datasets as well as typical model protection methods.
Executive Summary
The article titled 'Protecting Intellectual Property With Reliable Availability of Learning Models in AI-Based Cybersecurity Services' introduces a novel model locking (M-LOCK) scheme to enhance the availability protection of deep neural networks (DNNs) in AI-based cybersecurity services. The scheme ensures that DNNs produce poor accuracy without a specific token, thereby protecting intellectual property. The authors propose a data poisoning-based model manipulation (DPMM) method to train token-sensitive decision-making boundaries in DNNs. Extensive experiments demonstrate the scheme's reliability and effectiveness across various datasets and model protection methods.
Key Points
- ▸ Introduction of a novel model locking (M-LOCK) scheme for DNNs
- ▸ Proposal of a data poisoning-based model manipulation (DPMM) method
- ▸ High reliability and effectiveness demonstrated through extensive experiments
Merits
Innovative Approach
The M-LOCK scheme is a novel approach to intellectual property protection in DNNs, ensuring that models produce poor accuracy without a specific token.
Effective Protection
The scheme actively protects models' intellectual property copyright at each query, making it difficult for pirated models to function accurately.
Comprehensive Testing
The authors conducted extensive experiments across various benchmark datasets and typical model protection methods, demonstrating the scheme's reliability and effectiveness.
Demerits
Potential Overhead
The additional verification process during DNN inference might introduce computational overhead, which could impact the performance of AI-based cybersecurity services.
Complexity
The implementation of the M-LOCK scheme and DPMM method might be complex, requiring significant expertise and resources.
Limited Scope
The study focuses on AI-based cybersecurity services, and the applicability of the M-LOCK scheme to other domains remains unexplored.
Expert Commentary
The article presents a significant advancement in the field of intellectual property protection for AI-based cybersecurity services. The M-LOCK scheme and DPMM method offer a promising solution to the challenges posed by model piracy. The extensive experiments conducted by the authors lend credibility to their claims of reliability and effectiveness. However, the potential overhead and complexity of implementation should be carefully considered. The study also highlights the need for further research to explore the applicability of the M-LOCK scheme in other domains beyond cybersecurity. Overall, this article makes a valuable contribution to the ongoing discourse on AI security and intellectual property protection.
Recommendations
- ✓ Further research should be conducted to assess the computational overhead and performance impact of the M-LOCK scheme in real-world applications.
- ✓ Exploration of the applicability of the M-LOCK scheme in other domains beyond AI-based cybersecurity services.
