research
∙
03/27/2023
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Deep Neural Networks (DNN) are vulnerable to adversarial perturbations-s...
share
research
∙
05/14/2021
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinti...
research
∙
02/19/2020
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial top...
research
∙
02/19/2020
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Recurrent neural networks (RNNs) based automatic speech recognition has ...
research
∙
02/18/2020
Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of mo...
research
∙
02/18/2020
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs)...
research
∙
08/20/2019
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Despite achieving remarkable success in various domains, recent studies ...
research
∙
05/28/2019
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Despite the great achievements of deep neural networks (DNNs), the vulne...
research
∙
12/12/2018
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
research
∙
09/13/2018
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
research
∙
03/14/2018
