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.
Secure Mobile AI Models Against Attacks
Secure Mobile AI Models Against Attacks
This course is part of AI Security: Security in the Age of Artificial Intelligence Specialization
Instructors: Mark Peters
Access provided by Micron Technology
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Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
- Program Implementation
- Continuous Monitoring
- AI Security
- Application Security
- Mobile Security
- Security Requirements Analysis
- Mobile Development
- Apple iOS
- Information Privacy
- Threat Management
- Model Deployment
- System Monitoring
- Security Management
- Encryption
- Threat Modeling
- Skills section collapsed. Showing 9 of 15 skills.
Details to know
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Assessments
1 assignment
Taught in English
Recently updated!
December 2025
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This course is part of the AI Security: Security in the Age of Artificial Intelligence Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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