A survey on hardware security of DNN models and accelerators

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works

Towards Deep Learning Models Resistant to Adversarial Attacks

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602 · see citing works

The Limitations of Deep Learning in Adversarial Settings

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993 · see citing works

2011 International Conference on Computer Vision

(2011) · doi:10.1109/iccv17851.2011 · Cited by 2,895 · see citing works

ZOO

Chen, P., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140448 · Cited by 1,782 · see citing works

The 26th USENIX Security Symposium 2017

Αλεξόπουλος, Ν., Kiayias, A., Zacharias, T., & Talviste, R. (2017) · USENIX Security Symposium · Cited by 1,168 · see citing works

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

(2021) · doi:10.1109/cvpr46437.2021 · Cited by 839 · see citing works

Anatomy of high-performance matrix multiplication

Goto, K., & Geijn, R. A. (2008) · ACM Transactions on Mathematical Software · doi:10.1145/1356052.1356053 · Cited by 751 · see citing works

2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)

(2018) · doi:10.23919/date42667.2018 · Cited by 683 · see citing works

2019 IEEE International Symposium on Circuits and Systems (ISCAS)

(2019) · doi:10.1109/iscas43240.2019 · Cited by 507 · see citing works

A survey of FPGA-based accelerators for convolutional neural networks

Mittal, S. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3761-1 · Cited by 433 · see citing works

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

Rakin, A. S., He, Z., & Fan, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00130 · Cited by 275 · see citing works

Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks

Hua, W., Zhang, Z., & Suh, G. E. (2018) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3195970.3196105 · Cited by 256 · see citing works

I Know What You See

Wei, L., Luo, B., Li, Y., Liu, Y., & Xu, Q. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274696 · Cited by 206 · see citing works

TBT: Targeted Neural Network Attack With Bit Trojan

Rakin, A. S., He, Z., & Fan, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01321 · Cited by 200 · see citing works

A survey of techniques for optimizing deep learning on gpus

Mittal, S., & Vaishay, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101635 · Cited by 197 · see citing works

Rendered Insecure

Naghibijouybari, H., Neupane, A., Qian, Z., & Abu‐Ghazaleh, N. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243831 · Cited by 186 · see citing works

{CSI} {NN}: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel

Batina, L., Bhasin, S., Jap, D., & Picek, S. (2019) · Research Repository (Delft University of Technology) · Cited by 184 · see citing works

A survey on modeling and improving reliability of DNN algorithms and accelerators

Mittal, S. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101689 · Cited by 175 · see citing works

2021 58th ACM/IEEE Design Automation Conference (DAC)

(2021) · doi:10.1109/dac18074.2021 · Cited by 168 · see citing works

Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN ArchitecturesPreprint

Yan, M., Fletcher, C. W., & Torrellas, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04761 · Cited by 152 · see citing works

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault AttacksPreprint

Hong, S., Frigo, P., Kaya, Y., Giuffrida, C., & Dumitraş, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01017 · Cited by 151 · see citing works

Adversarial Robustness vs. Model Compression, or Both?

Ye, S., Xu, K., Liu, S., Cheng, H., Lambrechts, J.-H., Zhang, H., Zhou, A., Ma, K., Wang, Y., & Lin, X. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00020 · Cited by 144 · see citing works

A survey of deep learning techniques for vehicle detection from UAV images

Srivastava, S., Narayan, S., & Mittal, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102152 · Cited by 142 · see citing works

Practical Fault Attack on Deep Neural Networks

Breier, J., Hou, X., Jap, D., Ма, Л., Bhasin, S., & Liu, Y. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3278519 · Cited by 131 · see citing works

The 2022 International Conference on Computer-Aided Design (ICCAD)

Mitra, T. (2023) · IEEE Design and Test · doi:10.1109/mdat.2023.3238340 · Cited by 129 · see citing works

DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage

Yu, H., Ma, H., Yang, K., Zhao, Y., & Jin, Y. (2020) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/host45689.2020.9300274 · Cited by 121 · see citing works

Stealing Neural Networks via Timing Side ChannelsPreprint

Duddu, V., Samanta, D., Rao, D. V., & Bălaş, V. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11720 · Cited by 115 · see citing works

MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection

Dubey, A., Cammarota, R., & Aysu, A. (2020) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/host45689.2020.9300276 · Cited by 110 · see citing works

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

Yao, F., Rakin, A. S., & Fan, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13746 · Cited by 107 · see citing works

Leaky DNN: Stealing Deep-Learning Model Secret with GPU Context-Switching Side-Channel

Wei, J., Zhang, Y., Zhou, Z. G., Li, Z., & Faruque, M. A. A. (2020) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn48063.2020.00031 · Cited by 99 · see citing works

Open DNN Box by Power Side-Channel Attack

Xiang, Y., Chen, Z., Chen, Z., Fang, Z., Hao, H., Chen, J., Liu, Y., Wu, Z., Xuan, Q., & Yang, X. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.2973007 · Cited by 97 · see citing works

Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack

He, Z., Rakin, A. S., Li, J., Chakrabarti, C., & Fan, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01410 · Cited by 85 · see citing works

Hardware Trojan Design on Neural Networks

Clements, J., & Lao, Y. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702493 · Cited by 83 · see citing works

Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel AttacksPreprint

Hong, S., Davinroy, M., Kaya, Y., Locke, S. N., Rackow, I., Kulda, K., Dachman-Soled, D., & Dumitraş, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03487 · Cited by 75 · see citing works

Hardware Trojan Attacks on Neural NetworksPreprint

Clements, J., & Lao, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05768 · Cited by 73 · see citing works

2018 IEEE Security and Privacy Workshops (SPW)

(2018) · doi:10.1109/spw45269.2018 · Cited by 73 · see citing works

The Melbourne Shuffle: Improving Oblivious Storage in the Cloud

Ohrimenko, O., Goodrich, M. T., Tamassia, R., & Upfal, E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43951-7_47 · Cited by 68 · see citing works

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

(2019) · doi:10.1109/smartiot46493.2019 · Cited by 63 · see citing works

A Survey of Techniques for Cache Partitioning in Multicore Processors

Mittal, S. (2017) · ACM Computing Surveys · doi:10.1145/3062394 · Cited by 62 · see citing works

Hu-Fu: Hardware and Software Collaborative Attack Framework Against Neural Networks

Li, W., Yu, J., Ning, X., Wang, P., Wei, Q., Wang, Y., & Yang, H. (2018) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2018.00093 · Cited by 60 · see citing works

Defending Bit-Flip Attack through DNN Weight Reconstruction

Li, J., Rakin, A. S., Xiong, Y., Chang, L., He, Z., Fan, D., & Chakrabarti, C. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218665 · Cited by 59 · see citing works

A survey of spintronic architectures for processing-in-memory and neural networks

Umesh, S., & Mittal, S. (2018) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2018.11.005 · Cited by 57 · see citing works

A survey On hardware accelerators and optimization techniques for RNNs

Mittal, S., & Umesh, S. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101839 · Cited by 57 · see citing works

SNIFF: Reverse Engineering of Neural Networks With Fault Attacks

Breier, J., Jap, D., Hou, X., Bhasin, S., & Liu, Y. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3105697 · Cited by 56 · see citing works

A survey of accelerator architectures for 3D convolution neural networks

Mittal, S., & Vibhu, V. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102041 · Cited by 55 · see citing works

I Know What You See: Power Side-Channel Attack on Convolutional Neural Network AcceleratorsPreprint

Wei, L., Luo, B., Li, Y., Liu, Y., & Xu, Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05847 · Cited by 54 · see citing works

Imperceptible Misclassification Attack on Deep Learning Accelerator by Glitch Injection

Liu, W., Chang, C.-H., Zhang, F., & Lou, X. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218577 · Cited by 54 · see citing works

Memory Trojan Attack on Neural Network Accelerators

Zhao, Y. K., Hu, X., Li, S., Ye, J., Deng, L., Ji, Y., Xu, J., Wu, D., & Xie, Y. (2019) · Design, Automation, and Test in Europe · doi:10.23919/date.2019.8715027 · Cited by 53 · see citing works

Reverse-Engineering Deep Neural Networks Using Floating-Point Timing Side-Channels

Gongye, C., Fei, Y., & Wahl, T. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218707 · Cited by 42 · see citing works

2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

(2018) · doi:10.1109/isvlsi43036.2018 · Cited by 39 · see citing works

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural HintsPreprint

Hu, X., Liang, L., Deng, L., Li, S., Xie, X., Ji, Y., Ding, Y., Liu, C., Sherwood, T., & Xie, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03916 · Cited by 38 · see citing works

Hardware Trojan in FPGA CNN Accelerator

Ye, J., Hu, Y. H., & Li, X. (2018) · Proceedings - Asian Test Symposium/Proceedings · doi:10.1109/ats.2018.00024 · Cited by 37 · see citing works

Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation FunctionsPreprint

Rakin, A. S., Yi, J., Gong, B., & Fan, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06714 · Cited by 35 · see citing works

Floating-Point Multiplication Timing Attack on Deep Neural Network

Dong, G., Wang, P., Chen, P., Gu, R., & Hu, H. (2019) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot.2019.00032 · Cited by 35 · see citing works

SIN2: Stealth infection on neural network — A low-cost agile neural Trojan attack methodology

Liu, T., Wen, W., & Jin, Y. (2018) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/hst.2018.8383920 · Cited by 33 · see citing works

NPUFort

Wang, X., Hou, R., Zhu, Y., Zhang, J., & Meng, D. (2019) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3310273.3323070 · Cited by 33 · see citing works

Mitigating Reverse Engineering Attacks on Deep Neural Networks

Liu, Y., Dachman-Soled, D., & Srivastava, A. (2019) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2019.00122 · Cited by 31 · see citing works

DNNGuard: An Elastic Heterogeneous DNN Accelerator Architecture against Adversarial Attacks

Wang, X., Hou, R., Zhao, B., Yuan, F., Zhang, J., Meng, D., & Qian, X. (2020) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3373376.3378532 · Cited by 31 · see citing works

A Survey of Techniques for Cache Locking

Mittal, S. (2016) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/2858792 · Cited by 31 · see citing works

GANRED

Liu, Y., & Srivastava, A. (2020) · Cloud Computing Security Workshop · doi:10.1145/3411495.3421356 · Cited by 30 · see citing works

Enabling Secure NVM-Based in-Memory Neural Network Computing by Sparse Fast Gradient Encryption

Cai, Y., Chen, X., Tian, L., Wang, Y., & Yang, H. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3017870 · Cited by 29 · see citing works

A Survey of Techniques for Improving Security of GPUs

Mittal, S., Abhinaya, S. B., Reddy, M., & Ali, I. (2018) · Journal of Hardware and Systems Security · doi:10.1007/s41635-018-0039-0 · Cited by 29 · see citing works

Sequence Triggered Hardware Trojan in Neural Network Accelerator

Liu, Z., Ye, J., Hu, X., Li, H., Li, X., & Hu, Y. H. (2020) · IEEE VLSI Test Symposium (VTS) · doi:10.1109/vts48691.2020.9107582 · Cited by 24 · see citing works

Deep RNN-Oriented Paradigm Shift through BOCANet

Tehranipoor, F., Karimian, N., Kermani, M. M., & Mahmoodi, H. (2019) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3299874.3318031 · Cited by 24 · see citing works

2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

(2019) · doi:10.1109/host45642.2019 · Cited by 21 · see citing works

CURATING: A multi-objective based pruning technique for CNNs

Pattanayak, S., Nag, S., & Mittal, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102031 · Cited by 19 · see citing works

PUF Based Pay-Per-Device Scheme for IP Protection of CNN Model

Guo, Q., Ye, J., Gong, Y., Hu, Y. H., & Li, X. (2018) · Proceedings - Asian Test Symposium/Proceedings · doi:10.1109/ats.2018.00032 · Cited by 19 · see citing works

A Survey of Techniques for Improving Security of Non-volatile Memories

Mittal, S., & Alsalibi, A. I. (2018) · Journal of Hardware and Systems Security · doi:10.1007/s41635-018-0034-5 · Cited by 19 · see citing works

Poster

Batina, L., Bhasin, S., Jap, D., & Picek, S. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363280 · Cited by 19 · see citing works

2019 IEEE 28th Asian Test Symposium (ATS)

(2019) · doi:10.1109/ats47505.2019 · Cited by 19 · see citing works

Preventing Neural Network Model Exfiltration in Machine Learning Hardware Accelerators

Isakov, M., Bu, L., Cheng, H. Y., & Kinsy, M. A. (2018) · Asian Hardware Oriented Security and Trust Symposium (AsianHOST) · doi:10.1109/asianhost.2018.8607161 · Cited by 17 · see citing works

IEEE International Conference on VLSI Design

Singh, J., Mathew, J., Mohanty, S. P., & Pradhan, D. K. (2009) · Cited by 17 · see citing works

Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and CompactnessPreprint

Rakin, A. S., He, Z., Li, Y., Wang, Y., Wang, L., & Fan, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.13074 · Cited by 16 · see citing works

A Stealthy Hardware Trojan Exploiting the Architectural Vulnerability of Deep Learning Architectures: Input Interception Attack (IIA)Preprint

Odetola, T. A., Mohammed, H., & Hasan, S. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00783 · Cited by 16 · see citing works

Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

Luo, M., Myers, A. C., & Suh, G. E. (2020) · USENIX Security Symposium · Cited by 14 · see citing works

Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance

Jha, N. K., & Mittal, S. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3015531 · Cited by 14 · see citing works

2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

(2020) · doi:10.1109/asianhost51057.2020 · Cited by 14 · see citing works

The Ramifications of Making Deep Neural Networks Compact

Jha, N. K., Mittal, S., & Mattela, G. (2019) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 13 · see citing works

GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning.Preprint

Hua, W., Umar, M., Zhang, Z., & Suh, G. E. (2020) · arXiv (Cornell University) · Cited by 13 · see citing works

AEP: An error-bearing neural network accelerator for energy efficiency and model protection

Zhao, L., Zhang, Y., & Yang, J. J. (2017) · International Conference on Computer Aided Design · doi:10.1109/iccad.2017.8203897 · Cited by 12 · see citing works

Side Channel Attacks in Computation Offloading Systems with GPU Virtualization

Liu, S., Wei, Y., Chi, J., Shezan, F. H., & Tian, Y. (2019) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2019.00037 · Cited by 12 · see citing works

DeepPeep

Jha, N. K., Mittal, S., Kumar, B., & Mattela, G. (2020) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3414552 · Cited by 11 · see citing works

Hardware-Oriented Security and Trust - HOST 2008

Groszschaedl, J., Vejda, T., & Page, D. (2008) · The Institute of Electrical and Electronics Engineers · Cited by 11 · see citing works

QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural NetworksPreprint

Panda, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11233 · Cited by 11 · see citing works

2020 IEEE 38th VLSI Test Symposium (VTS)

(2020) · doi:10.1109/vts48691.2020 · Cited by 11 · see citing works

Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit AttackPreprint

Tehranipoor, F., Karimian, N., Kermani, M. M., & Mahmoodi, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.03332 · Cited by 10 · see citing works

2008 ACM international Conference on Computing Frontiers

(2008) · Kybernetes · doi:10.1108/k.2008.06737aac.004 · Cited by 9 · see citing works

E2GC: Energy-efficient Group Convolution in Deep Neural Networks

Jha, N. K., Saini, R., Mittal, S., & et al (2019) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 7 · see citing works

Vulnerability Analysis on Noise-Injection Based Hardware Attack on Deep Neural Networks

Liu, W., Wang, S., & Chang, C.-H. (2019) · Asian Hardware Oriented Security and Trust Symposium (AsianHOST) · doi:10.1109/asianhost47458.2019.9006698 · Cited by 6 · see citing works

Hardware-Software Co-Design Based Obfuscation of Hardware Accelerators

Chakraborty, A., & Srivastava, A. (2019) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2019.00104 · Cited by 6 · see citing works

2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

(2019) · doi:10.1109/asianhost47458.2019 · Cited by 6 · see citing works

DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs

Jha, N. K., Mittal, S., Kumar, B., & Mattela, G. (2020) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · doi:10.48550/arxiv.2007.15248 · Cited by 5 · see citing works

Experimental Evaluation of Deep Neural Network Resistance Against Fault Injection Attacks.Preprint

Hou, X., Breier, J., Jap, D., Ma, L., Bhasin, S., & Liu, Y. (2019) · IACR Cryptology ePrint Archive · Cited by 4 · see citing works

MgX: Near-Zero Overhead Memory Protection with an Application to Secure DNN AccelerationPreprint

Hua, W., Umar, M., Zhang, Z., & Suh, G. E. (2020) · arXiv (Cornell University) · Cited by 4 · see citing works

SEALing Neural Network Models in Secure Deep Learning AcceleratorsPreprint

Zuo, P., Yu, H., Liang, L., Xie, X., Hu, X., & Xie, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.03752 · Cited by 4 · see citing works

Stealing Your Data from Compressed Machine Learning Models

Xu, N., Liu, Q., Liu, T., Liu, Z., Guo, X., & Wen, W. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218633 · Cited by 3 · see citing works

GANRED GAN-based Reverse Engineering of DNNs via Cache Side-ChannelPreprint

Liu, Y., & Srivastava, A. (2020) · IACR Cryptology ePrint Archive · Cited by 3 · see citing works

Adversarial Sparsity Attacks on Deep Neural Networks.Preprint

Krithivasan, S., Sen, S., & Raghunathan, A. (2020) · arXiv (Cornell University) · Cited by 1 · see citing works

A Survey of Techniques for Optimizing Transformer Inference

Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works

A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions

Senevirathna, T., La, V. H., Marchal, S., Siniarski, B., Liyanage, M., & Wang, S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3437248 · Cited by 115 · see citing works

Physical Side-Channel Attacks on Embedded Neural Networks: A Survey

Real, M. M., & Salvador, R. (2021) · Applied Sciences · doi:10.3390/app11156790 · Cited by 52 · see citing works

Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges

Chen, H., & Babar, M. A. (2023) · ACM Computing Surveys · doi:10.1145/3638531 · Cited by 50 · see citing works

Disruptive attacks on artificial neural networks: A systematic review of attack techniques, detection methods, and protection strategies

Alobaid, A., Bonny, T., & Alrahhal, M. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200529 · Cited by 23 · see citing works

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

Dong, C., Yao, Y., Xu, Y., Liu, X., Wang, Y., Zhang, H., & Xu, L. (2023) · Sensors · doi:10.3390/s23125503 · Cited by 21 · see citing works

A survey of hardware architectures for generative adversarial networks

Shrivastava, N., Hanif, M. A., Mittal, S., Sarangi, S. R., & Shafique, M. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102227 · Cited by 17 · see citing works

A Side-Channel Hardware Trojan Detection Method Based on Fuzzy C-Means Clustering and Fusion Distance Algorithms

He, C., Lei, D., Wu, H. Y., Cheng, L., Yan, G., & Huang, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3339488 · Cited by 13 · see citing works

Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

Duy, K. D., Noh, T., Huh, S., & Lee, H. (2021) · IEEE Access · doi:10.1109/access.2021.3136889 · Cited by 14 · see citing works

Inferring DNN layer-types through a Hardware Performance Counters based Side Channel Attack

Kumar, B. A. D., Sai Chandra Teja, R., Mittal, S., Panda, B., & Mohan, C. K. (2021) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3486001.3486224 · Cited by 12 · see citing works

Trusted Deep Neural Execution—A Survey

Babar, M. F., & Hasan, M. (2023) · IEEE Access · doi:10.1109/access.2023.3274190 · Cited by 10 · see citing works

HWGN$$^{2}$$: Side-Channel Protected NNs Through Secure and Private Function Evaluation

Hashemi, M., Roy, S., Forte, D. J., & Ganji, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22829-2_13 · Cited by 8 · see citing works

Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs

Khare, Y., Lakara, K., Inukonda, M. S., Mittal, S., Chandra, M., & Kaushik, A. (2022) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc54232.2022.9888943 · Cited by 7 · see citing works

Deep Learning on RISC-V Platforms at the Edge: A Perspective on the Hardware and Software Support

Agosta, G., Galimberti, A., & Zoni, D. (2025) · ACM Computing Surveys · doi:10.1145/3772277 · Cited by 5 · see citing works

An Overview of Medical Electronic Hardware Security and Emerging Solutions

Taheri, S., & Asadizanjani, N. (2022) · Electronics · doi:10.3390/electronics11040610 · Cited by 5 · see citing works

An efficient technique to predict DC characteristics of nano-FinFETs using a deep neural network

Memon, Q.-U.-D., Munir, U., Ahmed, U. F., & Ahmed, M. M. (2022) · Journal of Computational Electronics · doi:10.1007/s10825-022-01978-7 · Cited by 5 · see citing works

Evaluating the impact of Permanent Faults in a GPU running a Deep Neural Network

Guerrero-Balaguera, J.-D., Galasso, L., Sierra, R. L., Sánchez, E., & Reorda, M. S. (2022) · IEEE International Test Conference in Asia (ITC-Asia) · doi:10.1109/itcasia55616.2022.00027 · Cited by 5 · see citing works

Hardware security of autonomous vehicles

Khalid, F., & Hasan, S. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99545-0.00012-9 · Cited by 4 · see citing works

Accelerating convolutional neural networks on FPGA platforms: a high-performance design methodology using OpenCL

Gdaim, S., & Mtibaa, A. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01647-5 · Cited by 4 · see citing works

Nacc-Guard: a lightweight DNN accelerator architecture for secure deep learning

Li, P., Che, C., & Hou, R. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05671-9 · Cited by 4 · see citing works

Systematic Literature Review of EM-SCA Attacks on EncryptionPreprint

Zunaidi, M. R., Scanlon, M., & Sayakkara, A. (2024) · doi:10.36227/techrxiv.170831690.06196196/v1 · Cited by 4 · see citing works

SAMURAI: A Framework for Safeguarding Against Malicious Usage and Resilience of AI

Rahaman, H., Chatterjee, A., & Bhunia, S. (2024) · Proceedings - Asian Test Symposium/Proceedings · doi:10.1109/ats64447.2024.10915409 · Cited by 3 · see citing works

Int-Monitor: a model triggered hardware trojan in deep learning accelerators

Li, P., & Hou, R. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04759-y · Cited by 3 · see citing works

Effective fault simulation of GPU’s permanent faults for reliability estimation of CNNs

Guerrero-Balaguera, J.-D., Sierra, R. L., & Reorda, M. S. (2022) · IEEE International On-Line Testing Symposium (IOLTS) · doi:10.1109/iolts56730.2022.9897823 · Cited by 3 · see citing works

CiMSAT: Exploiting SAT Analysis to Attack Compute-in-Memory Architecture Defenses

Wang, J., Yang, H., Deng, S., & Li, X. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690251 · Cited by 3 · see citing works

Spec17Tre: A New Dataset in Hardware Security and Using Deep Learning for Detecting Spectre Attacks

Aktas-Aydin, H., & Yalçın, G. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10215-9 · Cited by 2 · see citing works

A Review on AI Acceleration with FPGA and ASICs: A Comparative Study

Kotari, M., Sharath, Kanabargi, S., Alori, S., & Yogeshwara, B. (2025) · IEEE International Conference on Data, Decision and Systems (ICDDS) · doi:10.1109/icdds67737.2025.11344680 · Cited by 2 · see citing works

Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection

Greco, G., Cena, C., Albertin, U., Martini, M., & Chiaberge, M. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227924 · Cited by 2 · see citing works

FreqCounter: Efficient Cacheability of Encryption and Integrity Tree Counters in Secure Processors

Pandith, O. S., & Bashir, J. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102252 · Cited by 2 · see citing works

Analyzing the Impact of Bit-Flip Attacks on Extreme Learning Machine for Age-related Macular Degeneration Detection on OCT Volumes

Yang, C.-H., Liu, L., Lin, C.-H. D., Lu, C., Huang, T., & Wang, J.-K. (2024) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isct62336.2024.10791215 · Cited by 1 · see citing works

Hardware Security on Edge AI Devices

Yoshida, K., & Fujino, T. (2021) · IEICE ESS FUNDAMENTALS REVIEW · doi:10.1587/essfr.15.2_88 · Cited by 1 · see citing works

One-Time Programmable Memory for Ultra-Low Power ANN Inference Accelerator With Security Against Thermal Fault Injection

Deshmukh, S., Bende, A., Sanghai, D., Saraswat, V., Biswas, A., Kadam, A. A., Patil, S. V., Singh, A. K., Deshpande, V. V., & Ganguly, U. (2024) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2024.3508759 · Cited by 1 · see citing works

A Lightweight Memory Protection Scheme with Criticality-Aware Encryption and Embedded MAC for Secure DNN Accelerators

Lin, Y., & Lee, K.-J. (2024) · IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) · doi:10.1109/apccas62602.2024.10808709 · Cited by 1 · see citing works

RIPEMB: A framework for assessing hardware-assisted software security schemes in embedded systems

Tauner, S. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3539013

Algorithmic and Implementation-Based Threats for the Security of Embedded Machine Learning Models

Moëllic, P.-A., Dumont, M., Hector, K., Hennebert, C., Joud, R., & Paulin, D. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-54049-3_6

REDACTOR: eFPGA Redaction for DNN Accelerator Security

Baddour, Y., Hedayatipour, A., & Rezaei, A. (2025) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed65160.2025.11014341

Adversarial Robustness and Transferability on Stochastic Resistive Accelerators

Smagulova, K., Fouda, M. E., & Eltawil, A. M. (2025) · International Conference on Machine Intelligence and Smart Innovation (ICMISI) · doi:10.1109/icmisi65108.2025.11115660

Investigation of EM Fault Injection on Emerging Lightweight Neural Network Hardware

Goswami, B., Chetry, R., Chithambara Moorthii, J., & Suri, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01799-4_7

k-Sparse Audio Adversarial Attack: Budget-Constrained Audio Perturbations with Total Variation Regularization

Singh, A., Das, S., & Mittal, S. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11330479

FUSE: Frequency Spatial Ensemble Attack with Dual Source Attention

Nitwal, R. S., Aggarwal, M., & Mittal, S. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11331308

Developing a Deep Reinforcement Learning-Based Framework for Identifying Vulnerable Neurons Across Deep Neural Networks

Su, P., Yan, Z., & Chen, D. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3673605

SAMURAI: Runtime Attack Detection in AI Accelerators Using AI Performance Counters

Rahaman, H., Chatterjee, A., & Bhunia, S. (2026) · IEEE transactions on circuits and systems for artificial intelligence. · doi:10.1109/tcasai.2026.3674059 · Cited by 3 · see citing works

AI Production Process Security Attacks and Mitigation Techniques

Yu, H., & Halak, B. (2025) · Springer eBooks · doi:10.1007/978-3-032-08514-6_7

Dual-source attention-guided frequency-spatial ensemble attacks for cross-architecture adversarial transferability

Nitwal, R. S., Mittal, S., & Aggarwal, M. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103838

AI-Assisted Hardware Security Verification: A Survey and AI Accelerator Case Study

Hasan, K. T., Hasan, M. A., Alam, N., Islam, M. T., Das, U., & Farahmandi, F. (2026) · IEEE VLSI Test Symposium (VTS) · doi:10.1109/vts69484.2026.11563216

XAI-Driven Semantic Decoupling for NPU Fault Injection

Wu, J., Wei, X., Wang, S., & Namba, K. (2026) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas70754.2026.11672505

Efficient FPGA Acceleration of Deep Neural Networks for Image Processing Using an OpenCL-Based High-Level Synthesis Framework

Gdaim, S., Bensalem, H., & Gagnon, G. (2026) · Journal of Signal Processing Systems · doi:10.1007/s11265-026-02014-8

DMMU: SmartNIC-Based On-Device MMU for Scalable Virtualized I/O and Memory Overcommitment

Wu, C., Wang, Y., Mao, X., Lu, J., Zhang, R., Guo, K., Chen, C., Wu, J., Qi, Z., & Guan, H. (2026) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3847659

Deep Neural Network Security From a Hardware Perspective

Zhou, T., Zhang, Y., Duan, S., Luo, Y., & Xu, X. (2021) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1109/nanoarch53687.2021.9642246 · Cited by 3 · see citing works

Secure Machine Learning Hardware: Challenges and Progress [Feature]

Lee, K., Ashok, M., Maji, S., Agrawal, R., Joshi, A., Yan, M., Emer, J., & Chandrakasan, A. P. (2025) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2024.3509376 · Cited by 12 · see citing works

Security of Neural Networks from Hardware Perspective

Xu, Q., Arafin, M. T., & Qu, G. (2021) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1145/3394885.3431639 · Cited by 33 · see citing works

Towards Secure Machine Learning Acceleration: Threats and Defenses Across Algorithms, Architecture, and Circuits

Lee, K. (2024) · DSpace@MIT (Massachusetts Institute of Technology)

Security Overview

Chen, Y., Li, H., & Yang, H. (2026) · Springer eBooks · doi:10.1007/978-3-032-20979-5_17