Security Risk and Attacks in AI: A Survey of Security and Privacy
Security Risk and Attacks in AI: A Survey of Security and Privacy
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DOI：
10.1109/compsac57700.2023.00284
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Md Mostafizur Rahman;Aiasha Siddika Arshi;Md. Golam Moula Mehedi Hasan;Sumayia Farzana Mishu;Hossain Shahriar-Hossain-Sh
中科院分区：
文献类型：
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作者：
Md Mostafizur Rahman;Aiasha Siddika Arshi;Md. Golam Moula Mehedi Hasan;Sumayia Farzana Mishu;Hossain Shahriar-Hossain-Sh
这份调查报告概述了人工智能攻击的现状，以及随着人工智能在各种应用和服务中变得越来越普遍，人工智能安全和隐私面临的风险。与人工智能攻击和安全漏洞相关的风险越来越明显，并造成许多经济和社会损失。本文将对AI模型的不同类型的攻击进行分类，包括对抗性攻击、模型反演攻击、中毒攻击、数据中毒攻击、数据提取攻击和成员推断攻击。该文件还强调了开发安全和强大的AI模型以确保敏感数据的隐私和安全的重要性。通过系统的文献综述，本文全面分析了AI攻击的现状以及AI安全和隐私以及检测技术的风险。
This survey paper provides an overview of the current state of AI attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques.