75 citations · 654 across the 44 of their papers we have counts for
6 papers · 1 filter
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories
Adnan Siraj Rakin, Md Hafizul Islam Chowdhuryy, Fan Yao +1
Recent advancements of Deep Neural Networks (DNNs) have seen widespread deployment in multiple security-sensitive domains. The need of resource-intensive training and use of valuab…
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…
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…
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA
Adnan Siraj Rakin, Yukui Luo, Xiaolin Xu +1
The wide deployment of Deep Neural Networks (DNN) in high-performance cloud computing platforms brought to light multi-tenant cloud field-programmable gate arrays (FPGA) as a popul…
DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips
Fan Yao, Adnan Siraj Rakin, Deliang Fan
Security of machine learning is increasingly becoming a major concern due to the ubiquitous deployment of deep learning in many security-sensitive domains. Many prior studies have…
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,…