Taller de Adversarial Machine Learning
In this work the proposed defense strategy is evaluated against two black-box adversarial attacks, Hop Skip Jump and Square
Adversarial Machine Learning Attacks in Scaled Self-Driving Cars is the topic of my Ms thesis research at the University of Tartu
Test suite for machine learning models with approach for data security and blockchain.
An University Project for the AI4Cybersecurity class.
Linear regression and ML attack experiments.
Fork to preserve Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Evaluated the robustness of a deep face recognition model (InceptionResNetV1) against adversarial attacks. Tested multiple attack types, analyzed transferability, and implemented a defense system using specialized detectors to improve security while preserving accuracy.
Preprocessing and analysis of network data through unsupervised and supervised learning, with exploration of adversarial attacks on trained classifiers.
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