Machine Learning Security: Protecting Models from Adversarial Attacks and Data Poisoning
Format:
Paperback
En stock
0.29 kg
Sí
Nuevo
Amazon
USA
- Your AI is Learning. But Who is Teaching It?We trust machine learning models to drive our cars, filter our emails, and secure our bank accounts. But these models have a blind spot. A single pixel change can make a self-driving car see a green light as red. A hidden noise pattern can trick a voice assistant into opening a door.The "Black Box" is under attack.Machine Learning Security is the defensive manual for the era of AI. It moves beyond traditional cybersecurity—firewalls and encryption won’t save you here. This book exposes the new frontier of threats targeting the math itself: Adversarial Attacks, Data Poisoning, and Model Inversion.Written for data scientists and security engineers, this guide teaches you how to break your own models before someone else does. You will learn to identify vulnerabilities in training data, detect manipulation in real-time, and build "Robust AI" that can withstand active hostility.Inside, you will fortify your algorithms:Adversarial Evasion: Understand how attackers generate "perturbations"—invisible noise that fools classifiers—and how to use Adversarial Training to immune your models against them.Data Poisoning: Learn how malicious actors inject bad data into your training set to create "backdoors" that trigger only when they want them to.Privacy Attacks: Stop Model Inversion and Membership Inference attacks that allow hackers to reverse-engineer your model to steal sensitive user data.Defense Strategies: Implement defensive distillation, feature squeezing, and gradient masking to make your models harder to crack.Secure the Pipeline: Protect the entire ML lifecycle, from the Jupyter notebook to the production API endpoint.An accurate model is useless if it can be tricked. It’s time to build AI that isn’t just smart—it’s safe.Scroll up, click "Buy Now," and secure the intelligence of the future!
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