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05/09/2022
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
This work aims to tackle Model Inversion (MI) attack on Split Federated ...
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11/08/2021
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories
Recent advancements of Deep Neural Networks (DNNs) have seen widespread ...
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07/20/2021
NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing
Neural network stealing attacks have posed grave threats to neural netwo...
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01/20/2021
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
Adversarial attacks on Neural Network weights, such as the progressive b...
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11/05/2020
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA
The wide deployment of Deep Neural Networks (DNN) in high-performance cl...
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07/24/2020
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Deep Neural Network (DNN) attacks have mostly been conducted through adv...
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07/22/2020
Robust Machine Learning via Privacy/Rate-Distortion Theory
Robust machine learning formulations have emerged to address the prevale...
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03/30/2020
DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips
Security of machine learning is increasingly becoming a major concern du...
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09/10/2019
TBT: Targeted Neural Network Attack with Bit Trojan
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny ...
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05/30/2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Deep Neural Network (DNN) trained by the gradient descent method is know...
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03/28/2019
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Several important security issues of Deep Neural Network (DNN) have been...
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03/28/2019
Bit-Flip Attack: Crushing Neural Network withProgressive Bit Search
Several important security issues of Deep Neural Network (DNN) have been...
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11/22/2018
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack
Recent development in the field of Deep Learning have exposed the underl...
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07/18/2018
Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions
Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
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02/05/2018
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Deep learning algorithms and networks are vulnerable to perturbed inputs...
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02/05/2018
