TENSORFLOW AND SECURE AI PROTECTING ML MODELS IN ROBOTICS: Safeguarding Neural Networks with Encryption and Secure Inference - Softcover

Myles, Isandro; Halesworth, Corwin

Softcover

ISBN 13: 9798293551576

Publisher: Independently published, 2025

Synopsis

🔐 TensorFlow and Secure AI Protecting ML Models in Robotics

Safeguarding Neural Networks with Encryption and Secure Inference

In the world of robotics, AI isn’t just smart—it must be secure. As machine learning becomes the brain behind autonomous systems, the risk of data leakage, model theft, and adversarial attacks rises sharply.

TensorFlow and Secure AI is your essential guide to designing and deploying robust, encrypted, and privacy-preserving ML models in real-world robotics applications. Whether you’re building vision systems, navigation logic, or intelligent control, this book helps you protect the integrity and confidentiality of your models.

Inside, you'll learn how to:

• Encrypt TensorFlow models and secure model weights from reverse engineering

• Implement secure inference using homomorphic encryption and trusted execution environments (TEEs)

• Protect sensor data streams and model inputs from tampering and eavesdropping

• Defend against adversarial examples and model evasion attacks in robotics

• Apply secure model sharing practices in ROS2-powered distributed systems

• Use federated learning and differential privacy in collaborative robot networks

• Integrate TensorFlow with cryptographic libraries and hardware security modules (HSMs)

Whether you're deploying robots in healthcare, defense, manufacturing, or logistics, this book equips you with real-world strategies to keep your AI secure—inside and out.

🔐 For AI developers, robotics engineers, and embedded system architects focused on privacy and safety
🛡️ Includes TensorFlow code samples, secure deployment checklists, and threat modeling guides
📦 Supports ROS2, TFLite, and hardware-based security modules (TPM, ARM TrustZone, Intel SGX)

In a world of intelligent machines, security is no longer optional—it’s foundational.

Edition details

• Publisher: Independently published
• Publication date: 2025
• Language: English
• ISBN 13: 9798293551576
• Binding: Paperback
• Number of pages: 161