adversarial-machine-learning

Black Hat·31 min

• Black Hat
• AI Security
• Date: 2025
• Location: Black Hat

Security researchers examine technical aspects of Practical Defenses Against Adversarial Machine Learning.

Black Hat·44 min

• Black Hat
• AI Security
• Date: 2025
• Location: Black Hat

Presenters detail vulnerability discovery and exploitation in Zen and the Art of Adversarial Machine Learning.

CrikeyCon·42 min

• COMMUNITY
• AI Security
• Date: 2025
• Location: Community

Edward Prior

Presenters detail vulnerability discovery and exploitation in 2021 - Edward Prior - Introduction to Adversarial Machine Learning and other AI attacks.

DragonJAR - Hacking y Seguridad Informática·1 hr 16 min

• DRAGONJAR
• Blue Team
• Date: 2025
• Location: Dragonjar

Miguel Hernández Boza · José Ignacio Escribano Pabl

Demonstrates introducción al Adversarial Machine Learning and evaluates key implications for security practitioners.

IEEE Symposium on Security and Privacy·59 min

• IEEE SP
• AI Security
• Date: 2025
• Location: Ieee Sp

Nicholas Carlini

Analysts investigate attack surfaces and defenses concerning Nicholas Carlini: Making and Measuring Progress in Adversarial Machine Learning.

AI Village·12 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Zhao

Techniques and empirical results in adversarial training for hardening neural network models against evasion.

AI Village·17 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Xingxing Wei

Accelerating and stabilizing adversarial perturbation generation using momentum-based gradient optimization.

AI Village·37 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Li · Jiao

Leveraging open-source intelligence and public datasets for security research and AI model analysis.

AI Village·33 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Moustafa Alzantot · Yash Sharma

Practical black-box and gradient-free adversarial attack methodologies against machine learning systems.

AI Village·19 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Guo · Cuevas

An overview of recent progress and developments in adversarial deep learning attacks and defenses.

AI Village·22 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Rohan Taori · Ameya Kamsetty

Generating targeted adversarial perturbation examples against black-box automated speech recognition systems.

AI Village·28 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Weilin Xu

Adversarial physical and digital perturbations that make objects disappear from autonomous driving perception models.

AI Village·10 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Hou · Zhou

Creating transferable adversarial perturbations that reliably fool multiple disparate machine learning architectures.

AI Village·23 min

• DEF CON
• AI Security
• Date: Aug 2018
• Location: Las Vegas

Zhuang Zhang

Adapting and mutating malware samples using adversarial techniques to bypass machine-learning antivirus detectors.

Main Stage·44 min

• DEF CON
• AI Security
• Date: Aug 2016
• Location: Las Vegas

Clarence Chio

Clarence Chio explores adversarial perturbation techniques to evade deep neural network classifiers used in biometric authentication and malware detection.