4 citations · 4 across the 3 of their papers we have counts for
3 papers
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…
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…
Careful What You Wish For: on the Extraction of Adversarially Trained Models
Kacem Khaled, Gabriela Nicolescu, Felipe Gohring de Magalhães
Recent attacks on Machine Learning (ML) models such as evasion attacks with adversarial examples and models stealing through extraction attacks pose several security and privacy th…