ModelObfuscator: Obfuscating Model Information to Protect Deployed
ML-based Systems

ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems

1 June 2023

Papers citing "ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems"

10 / 10 papers shown

Title
Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentation Jack Highton Quok Zong Chong Samuel Finestone A. Beqiri Julia A. Schnabel K. Bhatia OOD 26 1 0 27 Jun 2024
Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud Sifat Ut Taki Spyridon Mastorakis FedML 27 1 0 02 Jun 2024
Investigating White-Box Attacks for On-Device Models M. Zhou Xiang Gao Jing Wu Kui Liu Hailong Sun Li Li AAML 34 9 0 08 Feb 2024
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey Xinyu She Yue Liu Yanjie Zhao Yiling He Li Li C. Tantithamthavorn Zhan Qin Haoyu Wang ELM 30 13 0 27 Oct 2023
Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization Stefan Trawicki William Hackett Lewis Birch M. Dascalu Peter Garraghan AAML 13 0 0 20 Sep 2023
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection Yuanchun Li Jiayi Hua Haoyu Wang Chunyang Chen Yunxin Liu FedML SILM 86 75 0 18 Jan 2021
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications Zhenpeng Chen Huihan Yao Yiling Lou Yanbin Cao Yuanqiang Liu Haoyu Wang Xuanzhe Liu 40 79 0 13 Jan 2021
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps Yujin Huang Han Hu Chunyang Chen AAML FedML 72 33 0 12 Jan 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,549 0 17 Apr 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,830 0 08 Jul 2016