46 citations · 173 across the 13 of their papers we have counts for
13 papers
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning
Chenan Wang, Pu Zhao, Siyue Wang +1
Deep Neural Network (DNN) models when implemented on executing devices as the inference engines are susceptible to Fault Injection Attacks (FIAs) that manipulate model parameters t…
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Ruyi Ding, Cheng Gongye, Siyue Wang +2
Deep Neural Networks (DNN) are vulnerable to adversarial perturbations-small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attac…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
Siyue Wang, Xiao Wang, Pin-Yu Chen +2
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-tr…
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Peiyan Dong, Siyue Wang, Wei Niu +8
Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniqu…
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Xiao Wang, Siyue Wang, Pin-Yu Chen +2
Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware…
Block Switching: A Stochastic Approach for Deep Learning Security
Xiao Wang, Siyue Wang, Pin-Yu Chen +2
Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. That is, subtly crafted perturbations of the input can make a trained network wit…
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
Pu Zhao, Pin-Yu Chen, Siyue Wang +1
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains…
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Xiao Wang, Siyue Wang, Pin-Yu Chen +4
Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on…
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Pu Zhao, Siyue Wang, Cheng Gongye +3
Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring hig…
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Zhe Li, Caiwen Ding, Siyue Wang +8
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Siyue Wang, Xiao Wang, Pu Zhao +4
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs…
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Yanzhi Wang, Zheng Zhan, Jiayu Li +6
Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of de…