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cs.LG2025
DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks
Kacem Khaled, Felipe Gohring de Magalhães, Gabriela Nicolescu
Convolutional Neural Networks (CNNs) and their quantized counterparts are vulnerable to extraction attacks, posing a significant threat of IP theft. Yet, the robustness of quantize…
cs.LG2023
Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks
Kacem Khaled, Mouna Dhaouadi, Felipe Gohring de Magalhães +1
Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellect…