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Editorial

Foreword to the Special Issue on Advances in Secure AI: Technology and Applications

by
Sangkyun Lee
School of Cybersecurity, Korea University, Seoul 02841, Korea
Appl. Sci. 2022, 12(19), 10015; https://doi.org/10.3390/app121910015
Submission received: 28 September 2022 / Accepted: 30 September 2022 / Published: 5 October 2022
(This article belongs to the Special Issue Advances in Secure AI: Technology and Applications)
Versions Notes
I am pleased to introduce the Special Issue on “Advances in Secure AI: Technology and Applications”. Artificial intelligence (AI) has grown as a key technology that enables numerous applications that facilitate our daily lives. However, this growth has set forth concerns about the safety, security, and reliability of the technology to be used for applications where the malfunctioning of AI can damage human beings or critical infrastructure. Since the findings of adversarial examples in deep neural networks [1], researchers have found many more ways to find adversarial examples (for example, FGSM [2], DeepFool [3], SBA [4], CW [5], and UAP [6], to name a few) and new types of vulnerabilities of deep neural networks such as model stealing [7,8] and data poisoning/backdoor attacks [9,10]. Additionally, it has been discovered that adversarial examples can work in physical environments [11]; for instance, making computer vision systems in autonomous vehicles recognize traffic signs incorrectly [12]. Secure AI is now studied in various fields of computer science beyond the AI community. For example, model stealing can also be done by side-channel attacks [13] and, therefore, would require considerations in secure computing based on multi-party computation [14] and hardware enclaves (e.g., Intel’s SGX and ARM’s TrustZone). Additionally, when distributed learning is preferred due to the cost and security issues of maintaining big data centers, the security of AI may have to be considered in the presence of federated learning [15] or homomorphic encryption [16], probably hardening data privacy via differential privacy [17]. Despite the recent discoveries, it is still not very well understood why certain weaknesses of AI models exist and how to strengthen them against specific exploits entirely. Moreover, as more AI models become available, testing them for vulnerabilities will be essential for trusting and using AI in mission-critical systems, where techniques like XAI (explainable AI) [18] could be applied to inspect logical or statistical issues in AI models.

Funding

This research has been supported by a Korea University Grant.

Conflicts of Interest

The author declares no conflict of interest.

References

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MDPI and ACS Style

Lee, S. Foreword to the Special Issue on Advances in Secure AI: Technology and Applications. Appl. Sci. 2022, 12, 10015. https://doi.org/10.3390/app121910015

AMA Style

Lee S. Foreword to the Special Issue on Advances in Secure AI: Technology and Applications. Applied Sciences. 2022; 12(19):10015. https://doi.org/10.3390/app121910015

Chicago/Turabian Style

Lee, Sangkyun. 2022. "Foreword to the Special Issue on Advances in Secure AI: Technology and Applications" Applied Sciences 12, no. 19: 10015. https://doi.org/10.3390/app121910015

APA Style

Lee, S. (2022). Foreword to the Special Issue on Advances in Secure AI: Technology and Applications. Applied Sciences, 12(19), 10015. https://doi.org/10.3390/app121910015

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