38 citations · 213 across the 19 of their papers we have counts for
19 papers
Model Extraction Attacks on Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +4
Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulner…
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +3
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activat…
NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing
Jingtao Li, Zhezhi He, Adnan Siraj Rakin +2
Neural network stealing attacks have posed grave threats to neural network model deployment. Such attacks can be launched by extracting neural architecture information, such as lay…
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Fangxin Liu, Wenbo Zhao, Yilong Zhao +6
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities…
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
Jingtao Li, Adnan Siraj Rakin, Zhezhi He +2
Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of b…
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
Sen Lin, Li Yang, Zhezhi He +2
While deep learning has achieved phenomenal successes in many AI applications, its enormous model size and intensive computation requirements pose a formidable challenge to the dep…
A Progressive Sub-Network Searching Framework for Dynamic Inference
Li Yang, Zhezhi He, Yu Cao +1
Many techniques have been developed, such as model compression, to make Deep Neural Networks (DNNs) inference more efficiently. Nevertheless, DNNs still lack excellent run-time dyn…
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Li Yang, Zhezhi He, Junshan Zhang +1
Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as \textit{catastrophic forgetting}. While recent continual lea…
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Adnan Siraj Rakin, Zhezhi He, Jingtao Li +3
Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack. Recently, another dimension of adversarial attack, namely, atta…
TBT: Targeted Neural Network Attack with Bit Trojan
Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently,…
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Xiaolong Ma, Sheng Lin, Shaokai Ye +10
Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or S…
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin, Zhezhi He, Li Yang +3
Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the counte…
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated securit…
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack
Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Recent development in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an ad…
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation
Zhezhi He, Deliang Fan
In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation…
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy
Zhezhi He, Boqing Gong, Deliang Fan
Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become t…
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Adnan Siraj Rakin, Zhezhi He, Boqing Gong +1
Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend again…
Developing All-Skyrmion Spiking Neural Network
Zhezhi He, Deliang Fan
In this work, we have proposed a revolutionary neuromorphic computing methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN). Such proposed methodology is based on o…
Current Induced Dynamics of Multiple Skyrmions with Domain Wall Pair and Skyrmion-based Majority Gate Design
Zhezhi He, Shaahin Angizi, Deliang Fan
As an intriguing ultra-small particle-like magnetic texture, skyrmion has attracted lots of research interests in next-generation ultra-dense and low power magnetic memory/logic de…