DeepDIST: a black-box anti-collusion framework for secure distribution of deep models
Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer fr...
Main Authors: | Cheng, Hang, Li, Xibin, Wang, Huaxiong, Zhang, Xinpeng, Liu, Ximeng, Wang, Meiqing, Li, Fengyong |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/171797 |
Similar Items
-
Collusive Dominant-Strategy Truthfulness
by: Chen, Jinc, et al.
Published: (2011) -
Finding partners in crime? How transparency about managers' behavior affects employee collusion
by: Maas, Victor S., et al.
Published: (2021) -
Do Deep Neural Networks Suffer from Crowding?
by: Volokitin, Anna, et al.
Published: (2017) -
A deep branching solver for fully nonlinear partial differential equations
by: Nguwi, Jiang Yu, et al.
Published: (2024) -
Deep neural network-based bandwidth enhancement of photoacoustic data
by: Gutta, Sreedevi, et al.
Published: (2017)