Analysis of formal method for certification of Neural Network | Formal Verification for Safe AI | AGPH Books

Book Title : Analysis of formal method for certification of Neural Network | Formal Verification of Neural Networks | AGPH Books

The rapid integration of Artificial Intelligence into safety-critical domains, such as autonomous driving and medical diagnostics, has created an urgent need for reliability.

However, while Neural Networks power these advancements, their opaque nature introduces serious risks. In addition, their vulnerability to adversarial perturbations makes them unreliable in high-stakes environments where failure is unacceptable.

This book, Analysis of Formal Method for Certification of Neural Network, provides a rigorous examination of formal verification techniques.

Specifically, it focuses on certifying Convolutional Neural Networks (CNNs) against hidden threats. Moreover, it moves beyond traditional software testing and introduces mathematical frameworks like Abstract Interpretation to ensure safety guarantees.

Furthermore, the book presents a detailed analysis of the DeepPoly abstract domain. This technique effectively balances precision with scalability for deep learning systems.

Through experimental evaluation using the MNIST dataset, it examines CNN robustness across ReLU, Tanh, and Sigmoid activation functions.

Importantly, the study highlights a gap between empirical accuracy and provable safety. In other words, high performance does not always guarantee reliability under stress.

Additionally, it identifies key challenges such as unstable neuron activations and loose verification bounds.

Therefore, the book proposes a forward-looking approach using hybrid verification and verification-aware training. As a result, it serves as a valuable resource for researchers, engineers, and developers.

Ultimately, it helps bridge the gap between theoretical soundness and real-world deployment of trustworthy AI systems.

About the Author

Anand Swaroop 

Anand Swaroop, has received his M.Tech. in Artificial Intelligence & Data Science from Institute of Engineering & Technology, Lucknow (An autonomous institute of AKTU Lucknow). He is working as Data Science Engineer in Velocis System Pvt Ltd. Lucknow. He has published papers in reputed conferences. His area of interest is Formal Methods, Machine Learning and Deep Learning.

Abhishek singh
Abhishek singh, is an Assistant Professor in Computer Science and Engineering Department at Institute of Engineering and Technology Lucknow. His research area is broadly classified as Formal Methods, Algorithms and Machine learning. He has published many papers in journals and conferences. He has guided many M.Tech thesis and also has patent on his name.

Prof. Girish Chandra
Prof. Girish Chandra, is Professor and Head in Department of Computer Science and Engineering at Institute of Engineering and Technology Lucknow. He has more than 29 year of experience in academics. His research area is broadly classified as Formal Verification and Cryptography.

About The Publisher:

AGPH Books is a Professional Self Book Publishing House based in Central India, specializing in academic, professional, fiction, and non-fiction books in both print, digital and audio formats. The publishing house produces textbooks, research and reference works, biographies, self-help titles, children’s books, literary fiction, poetry, and general interest publications. With a transparent publishing process and strong digital distribution, AGPH Books ensures global availability through Google Books, Amazon, Flipkart, and its official website store, supporting authors and institutions in reaching a wide and diverse readership.