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.
Preprocessing and analysis of network data through unsupervised and supervised learning, with exploration of adversarial attacks on trained classifiers.
Bidirectional Security Framework for Human/LLM Interfaces - RC9-FPR4 baseline frozen (ASR 2.76%, Wilson Upper 3.59% GATE PASS, FPR stratified: doc_with_codefence 0.79% Upper GATE PASS, pure_doc 4.69% Upper). RC10.3c development integrated (semantic veto, experimental). Tests: 833/853 (97.7%), MyPy clean, CI GREEN. Shadow deployment ready.
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