Reverse Engineering of Deceptions on Machine- and Human-Centric Attacks
Seiten
2024
now publishers Inc (Verlag)
978-1-63828-340-9 (ISBN)
now publishers Inc (Verlag)
978-1-63828-340-9 (ISBN)
A comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. The monograph delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.
This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.
For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.
In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided.
This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.
For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.
In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided.
1. Introduction
2. Reverse Engineering of Adversarial Examples
3. Model Parsing via Adversarial Examples
4. Reverse Engineering of Generated Images
5. Manipulation Localization of Generated Images
6. Conclusion and Discussion
References
| Erscheinungsdatum | 04.04.2024 |
|---|---|
| Reihe/Serie | Foundations and Trends® in Privacy and Security |
| Verlagsort | Hanover |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 170 g |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| ISBN-10 | 1-63828-340-0 / 1638283400 |
| ISBN-13 | 978-1-63828-340-9 / 9781638283409 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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