Careful What You Wish For: on the Extraction of Adversarially Trained Models

21 July 2022

Papers citing "Careful What You Wish For: on the Extraction of Adversarially Trained Models"

6 / 6 papers shown

Title
Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks Daryna Oliynyk Rudolf Mayer Andreas Rauber AAML 44 0 0 08 Mar 2025
A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations Yulong Yang Haoran Fan Chenhao Lin Qian Li Zhengyu Zhao Chao Shen Xiaohong Guan AAML 43 0 0 12 Nov 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models Zhenhua Wang Wei Xie Kai Chen Baosheng Wang Zhiwen Gui Enze Wang AAML SILM 20 6 0 16 Aug 2023
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,833 0 08 Jul 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,194 0 01 Sep 2014