41 citations · 63 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…