The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and swift responses critical for diverse applications, its devices' intrinsic resource limitations expose them to security risks. This research delves into the adversarial vulnerabilities of AI models on resource-constrained embedded hardware, with a focus on Model Extraction and Evasion Attacks. Our findings reveal that adversarial attacks from powerful host machines could be transferred to smaller, less secure devices like ESP32 and Raspberry Pi. This illustrates that adversarial attacks could be extended to tiny devices, underscoring vulnerabilities, and emphasizing the necessity for reinforced security measures in TinyML deployments. This exploration enhances the comprehension of security challenges in TinyML and offers insights for safeguarding sensitive data and ensuring device dependability in AI-powered edge computing settings.

翻译：人工智能（AI）与机器学习（ML）的最新进展推动了TinyML的兴起，这一范式使得AI计算能够在边缘端独立于云端连接而执行。尽管TinyML为各类应用提供了关键性的实时数据分析与快速响应能力，但其设备固有的资源限制使其面临安全风险。本研究深入探讨了资源受限嵌入式硬件上AI模型的对抗性脆弱性，重点关注模型提取攻击与规避攻击。我们的研究结果表明，来自高性能主机的对抗性攻击可迁移至ESP32和树莓派等更小、安全性较低的设备。这证明对抗性攻击可扩展至微型设备，揭示了其脆弱性，并强调了在TinyML部署中加强安全措施的必要性。此项探索深化了对TinyML安全挑战的理解，并为在AI驱动的边缘计算环境中保护敏感数据、确保设备可靠性提供了见解。