Adnan Siraj Rakin

Md Hafizul Islam Chowdhuryy

Fan Yao

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

RepNet: Efficient On-Device Learning via Feature Reprogramming.

[BibT_eX]

[DOI]

Li Yang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

DA<sup>3</sup>: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning.

[BibT_eX]

[DOI]

Li Yang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy.

[BibT_eX]

[DOI]

CoRR, 2021

Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA.

[BibT_eX]

[DOI]

Proceedings of the 30th USENIX Security Symposium, 2021

Towards Universal Adversarial Examples and Defenses.

[BibT_eX]

[DOI]

Proceedings of the IEEE Information Theory Workshop, 2021

Robust Machine Learning via Privacy/ Rate-Distortion Theory.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2021

NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Hardware Oriented Security and Trust, 2021

RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery.

[BibT_eX]

[DOI]

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

Leveraging Noise and Aggressive Quantization of In-Memory Computing for Robust DNN Hardware Against Adversarial Input and Weight Attacks.

[BibT_eX]

[DOI]

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

2020

Sparse BD-Net: A Multiplication-less DNN with Sparse Binarized Depth-wise Separable Convolution.

[BibT_eX]

[DOI]

ACM J. Emerg. Technol. Comput. Syst., 2020

DA2: Deep Attention Adapter for Memory-EfficientOn-Device Multi-Domain Learning.

[BibT_eX]

[DOI]

Li Yang

CoRR, 2020

DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips.

[BibT_eX]

[DOI]

Fan Yao

Proceedings of the 29th USENIX Security Symposium, 2020

Robust Sparse Regularization: Defending Adversarial Attacks Via Regularized Sparse Network.

[BibT_eX]

[DOI]

Proceedings of the GLSVLSI '20: Great Lakes Symposium on VLSI 2020, 2020

Defending Bit-Flip Attack through DNN Weight Reconstruction.

[BibT_eX]

[DOI]

Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

TBT: Targeted Neural Network Attack With Bit Trojan.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness.

[BibT_eX]

[DOI]

CoRR, 2019

Bit-Flip Attack: Crushing Neural Network withProgressive Bit Search.

[BibT_eX]

[DOI]

CoRR, 2019

Defense-Net: Defend Against a Wide Range of Adversarial Attacks through Adversarial Detector.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE Computer Society Annual Symposium on VLSI, 2019

Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack.

[BibT_eX]

[DOI]