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Secure Mobile AI Models Against Attacks

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Secure Mobile AI Models Against Attacks

Ce cours fait partie de Spécialisation "AI Security: Security in the Age of Artificial Intelligence"

Instructeurs : Mark Peters

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Mobile Development
Catégorie : Program Implementation
Catégorie : Responsible AI
Catégorie : Hardening
Catégorie : AI Security
Catégorie : Anomaly Detection
Catégorie : Threat Management
Catégorie : Security Controls
Catégorie : Threat Detection
Catégorie : Secure Coding
Catégorie : Security Management
Catégorie : Application Security
Catégorie : System Monitoring
Catégorie : Encryption
Catégorie : Continuous Monitoring
Catégorie : Threat Modeling

Outils que vous découvrirez

Catégorie : Model Deployment
Catégorie : Mobile Security

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

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décembre 2025

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Ce cours fait partie de la Spécialisation "AI Security: Security in the Age of Artificial Intelligence"

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Il y a 3 modules dans ce cours

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.

Détails du module

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.

Inclus

4 vidéos2 lectures1 évaluation par les pairs

4 vidéosTotal 19 minutes

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

2 lecturesTotal 10 minutes

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

1 évaluation par les pairsTotal 25 minutes

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

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.

Inclus

3 vidéos1 lecture1 évaluation par les pairs

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.

Inclus

4 vidéos1 lecture1 devoir2 évaluations par les pairs

4 vidéosTotal 29 minutes

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

1 lectureTotal 5 minutes

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

1 devoirTotal 20 minutes

Secure Mobile AI Models Against Attacks20 minutes

2 évaluations par les pairsTotal 85 minutes

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

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