75 citations · 654 across the 44 of their papers we have counts for
14 papers · 1 filter
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…
TRGP: Trust Region Gradient Projection for Continual Learning
Sen Lin, Li Yang, Deliang Fan +1
Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of…
Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification
Jingbo Sun, Li Yang, Jiaxin Zhang +4
Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis an…
GROWN: GRow Only When Necessary for Continual Learning
Li Yang, Sen Lin, Junshan Zhang +1
Catastrophic forgetting is a notorious issue in deep learning, referring to the fact that Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning ne…
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Adnan Siraj Rakin, Li Yang, Jingtao Li +5
Recently developed adversarial weight attack, a.k.a. bit-flip attack (BFA), has shown enormous success in compromising Deep Neural Network (DNN) performance with an extremely small…
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…
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…
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…
Processing-In-Memory Acceleration of Convolutional Neural Networks for Energy-Efficiency, and Power-Intermittency Resilience
Arman Roohi, Shaahin Angizi, Deliang Fan +1
Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) computational sub-arrays…
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…
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
Adnan Siraj Rakin, Jinfeng Yi, Boqing Gong +1
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNN…
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…