3 papers
cs.CR2026
Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers
Parker Hummel, Ryne Skabo, Muhammad Abusaqer
This paper presents a reproducible, educational study of evasion attacks in image classification and text classification. A compact convolutional network trained on MNIST reached 9…
cs.CR2026
Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Toshif Khan, Muhammad Abusaqer
Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and…
cs.CR2026
Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2
William Novak, Muhammad Abusaqer
Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural language processing (NLP), where tr…