Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Adversarial ML & Security Threats is an advanced, research-driven training program that explores how malicious actors exploit weaknesses in machine learning systems. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Adversarial ML & Security Threats is an advanced, research-driven training program that explores how malicious actors exploit weaknesses in machine learning systems.
📋 Course Objectives
1. Put AI Professional Certification techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Professional Certification
• R&D engineers and working professionals applying AI Professional Certification in industry
• Academics and educators building research or teaching capacity in AI Professional Certification
🚀 Key Learning Outcomes
• A demonstrable AI Professional Certification project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Adversarial Machine Learning
Define Adversarial ML and its importance. • Outline historical context and emerging trends. • Categorize types of adversarial threats (white-box, black-box, gray-box). • Survey vulnerabilities within ML pipelines.
Attacks Against ML Models
Execute evasion attacks on diverse models (image, text, tabular). • Implement poisoning attacks during model training. • Perform model inversion and membership inference attacks. • Utilize leading adversarial ML tools (Foolbox, ART, CleverHans).
Defensive Strategies and Robust Model Design
Apply adversarial training techniques to enhance resilience. • Employ input preprocessing and gradient masking for defense. • Explore certified defenses and formal security guarantees. • Evaluate model robustness using specialized metrics.
Security in the ML Lifecycle
Design secure data pipelines and ensure label integrity. • Identify and mitigate attack surfaces in model deployment. • Conduct threat modeling for machine learning systems. • Implement secure MLOps practices and monitoring pipelines.
Real-World Applications and Future Challenges
Analyze real-world case studies of attacks on AI systems. • Investigate adversarial threats in federated learning and Edge AI. • Address legal, ethical, and compliance risks in AI security. • Practice AI red teaming and offensive security testing.
Capstone and Emerging Trends
Design and conceptualize an adversarial attack scenario. • Simulate and evaluate robust defense mechanisms. • Present a final capstone project showcasing applied skills. • Examine future directions in AI security and regulation.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Foolbox |
| Covered Tool / Platform | ART |
| Covered Tool / Platform | CleverHans |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
