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NGS Data Processing and Interpretation
1 × INR ₹4,999.00
Subtotal: INR ₹4,999.00
AED
UAD Dirham
Code: AED
AI · Moderate · Live online
Adversarial ML & Security Threats
Secure the Model—Understand, Detect, and Defend Against Adversarial AI Attacks
★ 4.2 (28 ratings)3 WeeksCertificate
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. As AI becomes central to decision-making in defense, finance, healthcare, and cybersecurity, understanding adversarial threats is essential. This course provides technical insights into how models can be tricked, poisoned, or reverse-engineered, and trains participants to build defenses against such attacks using robust ML practices, secure deployment methods, and adversarial training.
Aim
To develop advanced capabilities in identifying, analyzing, and mitigating adversarial machine learning (AML) attacks and AI-specific vulnerabilities in deployed ML systems, with a focus on real-world security threats in AI-enabled environments.
Program Objectives
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To bridge machine learning engineering with cybersecurity expertise
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To build capabilities for defending against real-world AI attacks
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To create secure, reliable, and resilient AI systems
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To train professionals for AI red teaming and adversarial simulation roles
Program Structure
Week 1: Foundations of Adversarial Machine Learning
Module 1: Introduction to Adversarial ML
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Chapter 1.1: What is Adversarial ML?
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Chapter 1.2: Historical Context and Emerging Importance
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Chapter 1.3: Types of Adversarial Threats (White-box, Black-box, Gray-box)
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Chapter 1.4: Overview of Vulnerabilities in ML Pipelines
Module 2: Attacks Against ML Models
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Chapter 2.1: Evasion Attacks on Image, Text, and Tabular Models
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Chapter 2.2: Poisoning Attacks During Training
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Chapter 2.3: Model Inversion and Membership Inference
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Chapter 2.4: Tools and Libraries (Foolbox, ART, CleverHans)
Week 2: Defensive Strategies and Robust Model Design
Module 3: Making Models Robust
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Chapter 3.1: Adversarial Training Techniques
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Chapter 3.2: Input Preprocessing and Gradient Masking
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Chapter 3.3: Certified Defenses and Formal Guarantees
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Chapter 3.4: Evaluation Metrics for Robustness
Module 4: Security in the ML Lifecycle
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Chapter 4.1: Secure Data Pipelines and Label Integrity
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Chapter 4.2: Attack Surface in Model Deployment
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Chapter 4.3: Threat Modeling for ML Systems
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Chapter 4.4: Secure MLOps and Monitoring Pipelines
Week 3: Real-World Applications and Future Challenges
Module 5: Adversarial ML in Practice
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Chapter 5.1: Case Studies: Attacks on Facial Recognition, NLP, and Healthcare Models
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Chapter 5.2: Adversarial Threats in Federated Learning and Edge AI
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Chapter 5.3: Legal, Ethical, and Compliance Risks
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Chapter 5.4: AI Red Teaming and Offensive Testing
Module 6: Capstone and Emerging Trends
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Chapter 6.1: Design Your Own Adversarial Attack Scenario
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Chapter 6.2: Simulate and Evaluate Defense Mechanisms
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Chapter 6.3: Final Capstone Project Presentation
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Chapter 6.4: Future Directions – AI Security, Regulation, and Red-Blue Team Dynamics
Who Should Enrol?
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AI/ML engineers, cybersecurity professionals, and researchers
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Graduate students and advanced learners in computer science or data science
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Proficiency in Python, ML frameworks (TensorFlow, PyTorch), and basic cybersecurity concepts is recommended
Program Outcomes
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Understand how adversarial attacks are executed and evaded
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Design, simulate, and analyze adversarial scenarios across modalities
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Develop and implement defenses to strengthen ML model security
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Assess AI systems for vulnerabilities and compliance risks
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Prepare for red-team exercises and real-world AML incidents
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy