Tag: Adversarial

December 9, 2024

Adversarial AI Attacks, Mitigations, and Defense Strategies

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Free Download Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps by John Sotiropoulos
English | July 26th, 2024 | ISBN: 1835087981 | 586 pages | True PDF | 25.70 MB
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST

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January 13, 2024

Computer Vision Object Detection in Adversarial Vision

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Free Download Computer Vision: Object Detection in Adversarial Vision
English | 2024 | ISBN: 1032551801 | 209 Pages | PDF (True) | 82 MB
This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of

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December 25, 2023

Machine Learning Algorithms Adversarial Robustness in Signal Processing (Wireless Networks)

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Free Download Machine Learning Algorithms: Adversarial Robustness in Signal Processing (Wireless Networks) by Fuwei Li, Lifeng Lai, Shuguang Cui
English | November 16, 2022 | ISBN: 3031163745 | 113 pages | MOBI | 11 Mb
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks.

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