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Secure Mobile AI Models Against Attacks

本课程是 AI Security: Security in the Age of Artificial Intelligence 专项课程的一部分

位教师：Mark Peters

包含在中

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3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Explain the fundamentals of deploying AI models on mobile applications, including their unique performance, privacy, and security considerations.
Analyze threats to mobile AI models like reverse engineering, adversarial attacks, and privacy leaks and their effect on reliability and trust.
Design a layered defense strategy for securing mobile AI applications by integrating encryption, obfuscation, and continuous telemetry monitoring.

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Security: Security in the Age of Artificial Intelligence 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有3个模块

AI models are no longer locked in the cloud—they live in your pocket, powering mobile apps for fitness, finance, healthcare, and beyond. But with this power comes new risk: adversarial attacks, model theft, privacy leaks, and silent failures that undermine user trust.

Securing Mobile AI Models against Attacks (SMAI) is a hands-on course for mobile app developers, AI engineers, and cybersecurity professionals who want to safeguard AI models on Android and iOS. Through interactive coach dialogues, video lessons, and practical labs, you’ll learn how to embed security from day one, analyze threats like reverse engineering and adversarial inputs, and implement layered defenses using encryption, obfuscation, and OpenTelemetry monitoring. By the end, you will have the skills to design, secure, and continuously monitor mobile AI applications, ensuring resilience, compliance, and user confidence in real-world deployments. Participants should have a basic understanding of AI, machine learning, and mobile development, along with knowledge of security concepts like encryption and data protection. Familiarity with AI model deployment and monitoring tools like OpenTelemetry is also helpful.

单元详情

This module introduces learners to the unique nature of AI models running on mobile devices and why security cannot be bolted on later. Through an AI-guided dialogue, short lessons, and a design-focused lab, learners see how early choices in packaging and deployment set the stage for resilience or vulnerability. In this module, the emphasis is that security is not a barrier to innovation, it is the enabler of sustainable mobile AI applications.

涵盖的内容

4个视频2篇阅读材料1次同伴评审

4个视频总计19分钟

An Introduction to Protecting Mobile AI Models4分钟
What Makes Mobile AI Different4分钟
From Model to Mobile App: The Deployment Pipeline5分钟
Why Security Matters From Day One6分钟

2篇阅读材料总计10分钟

Welcome to the Course: Course Overview5分钟
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks5分钟

1次同伴评审总计25分钟

Hands-On-Learning: Designing Early Security for Mobile AI Models25分钟

In this module, learners will dive deeply into the adversarial landscape, exploring how reverse engineering, data inference, and adversarial inputs compromise mobile AI systems. The AI coach uses a real-world scenario to show how curiosity can become an attack, while lessons and labs reveal the tangible risks of model theft and privacy leaks. Forwards the understanding that researching threats is not paranoia but the prerequisite for defending trust and intellectual property, the essential elements of a secure, and mobile, AI.

涵盖的内容

3个视频1篇阅读材料1次同伴评审

This module shifts from analysis to action, equipping learners with strategies to harden models and continuously monitor them in production. Guided by an AI dialogue on stealthy breaches, learners see how OpenTelemetry and layered defenses provide visibility and resilience in the field. Overall, learners discover securing mobile AI is not a one-time act, but a continuous practice of observing, adapting, and improving.

涵盖的内容

4个视频1篇阅读材料1个作业2次同伴评审

4个视频总计29分钟

Securing the Model Lifecycle6分钟
Continuous Monitoring and Telemetry13分钟
Building a Security Mindset for Mobile AI 6分钟
Delivering a Secure Mobile AI 3分钟

1篇阅读材料总计5分钟

A Meta-Survey of Adversarial Attacks Against Artificial Intelligence Algorithms, Including Diffusion Models5分钟

1个作业总计20分钟

Secure Mobile AI Models Against Attacks20分钟

2次同伴评审总计85分钟

Hands-On-Learning: Building Telemetry for AI Threat Detection with Open Telemetry25分钟
Project: Designing and Defending a Secure Mobile AI Ecosystem 60分钟