ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles

21 September 2020

Papers citing "ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles"

8 / 8 papers shown

Title
Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks Daryna Oliynyk Rudolf Mayer Andreas Rauber AAML 49 0 0 08 Mar 2025
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack Renyang Liu Jinhong Zhang Kwok-Yan Lam Jun Zhao Wei Zhou 25 1 0 15 Oct 2023
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks Jun Guo Aishan Liu Xingyu Zheng Siyuan Liang Yisong Xiao Yichao Wu Xianglong Liu AAML 38 12 0 02 Aug 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 52 50 0 18 May 2023
Careful What You Wish For: on the Extraction of Adversarially Trained Models Kacem Khaled Gabriela Nicolescu F. Magalhães MIACV AAML 35 4 0 21 Jul 2022
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences Daryna Oliynyk Rudolf Mayer Andreas Rauber 49 106 0 16 Jun 2022
TinyMLOps: Operational Challenges for Widespread Edge AI Adoption Sam Leroux Pieter Simoens Meelis Lootus Kartik Thakore Akshay Sharma 34 16 0 21 Mar 2022
Conditional Image Synthesis With Auxiliary Classifier GANs Augustus Odena C. Olah Jonathon Shlens GAN 250 3,191 0 30 Oct 2016