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Recent advancements of Deep Neural Networks (DNNs) have seen widespread deployment in multiple security-sensitive domains.
Y. Langsam, M. Augenstein, and A. M. Tenenbaum,
1996
Earlier work this paper cites.
M. Gorman,
2004
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
R. Benadjila, O. Billet, S. Gueron, and M. J. Robshaw, “The intel aes instructions set and the sha-3 candidates,” in
2009
Earlier work this paper cites.
A. Krizhevsky, V. Nair, and G. Hinton, “Cifar-10 (canadian institute for advanced research),”
2010
Earlier work this paper cites.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,”
2012
Earlier work this paper cites.
Y. Kim, R. Daly, J. Kim, C. Fallin, J. H. Lee, D. Lee, C. Wilkerson, K. Lai, and O. Mutlu, “Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,”
2014
Earlier work this paper cites.
Y. Yarom and K. Falkner, “Flush+ reload: A high resolution, low noise, l3 cache side-channel attack,” in
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Earlier work this paper cites.
F. Liu, Y. Yarom, Q. Ge, G. Heiser, and R. B. Lee, “Last-level cache side-channel attacks are practical,” in
2015
Earlier work this paper cites.
R. Callan, A. Zajić, and M. Prvulovic, “Fase: Finding amplitude-modulated side-channel emanations,” in
2015
Earlier work this paper cites.
M. Seaborn and T. Dullien, “Exploiting the dram rowhammer bug to gain kernel privileges,”
2015
Earlier work this paper cites.
M. Ribeiro, K. Grolinger, and M. A. Capretz, “Mlaas: Machine learning as a service,” in
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Gruss, C. Maurice, and S. Mangard, “Rowhammer. js: A remote software-induced fault attack in javascript,” in
2016
Earlier work this paper cites.
K. Razavi, B. Gras, E. Bosman, B. Preneel, C. Giuffrida, and H. Bos, “Flip feng shui: Hammering a needle in the software stack,” in
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,”
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
V. Costan and S. Devadas, “Intel sgx explained.”
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in
2017
Earlier work this paper cites.
F. Yao, G. Venkataramani, and M. Doroslovački, “Covert timing channels exploiting non-uniform memory access based architectures,” in
2017
Earlier work this paper cites.
Y. Jang, J. Lee, S. Lee, and T. Kim, “Sgx-bomb: Locking down the processor via rowhammer attack,” in
2017
Earlier work this paper cites.
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in
2017
Cited alongside, same era.
M. Orenbach, P. Lifshits, M. Minkin, and M. Silberstein, “Eleos: Exitless os services for sgx enclaves,” in
2017
Cited alongside, same era.
J. R. Correia-Silva, R. F. Berriel, C. Badue, A. F. de Souza, and T. Oliveira-Santos, “Copycat cnn: Stealing knowledge by persuading confession with random non-labeled data,” in
2018
Cited alongside, same era.
H. Naghibijouybari, A. Neupane, Z. Qian, and N. Abu-Ghazaleh, “Rendered insecure: Gpu side channel attacks are practical,” in
2018
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga
2019
Later among the works it cites.
H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, and M. I. Jordan, “Theoretically principled trade-off between robustness and accuracy,” in
2019
Later among the works it cites.
V. Chandrasekaran, K. Chaudhuri, I. Giacomelli, S. Jha, and S. Yan, “Exploring connections between active learning and model extraction,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
K. Murdock, D. Oswald, F. D. Garcia, J. Van Bulck, D. Gruss, and F. Piessens, “Plundervolt: Software-based fault injection attacks against intel sgx,” in
2020
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2018
Cited alongside, same era.
D. Evtyushkin, R. Riley, N. C. Abu-Ghazaleh, ECE, and D. Ponomarev, “Branchscope: A new side-channel attack on directional branch predictor,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Gruss, M. Lipp, M. Schwarz, D. Genkin, J. Juffinger, S. O’Connell, W. Schoechl, and Y. Yarom, “Another flip in the wall of rowhammer defenses,” in
2018
Cited alongside, same era.
F. Yao, M. Doroslovacki, and G. Venkataramani, “Are coherence protocol states vulnerable to information leakage?” in
2018
Cited alongside, same era.
R. K. Konoth, M. Oliverio, A. Tatar, D. Andriesse, H. Bos, C. Giuffrida, and K. Razavi, “Zebram: comprehensive and compatible software protection against rowhammer attacks,” in
2018
Cited alongside, same era.
A. Tatar, C. Giuffrida, H. Bos, and K. Razavi, “Defeating software mitigations against rowhammer: a surgical precision hammer,” in
2018
Cited alongside, same era.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in
2018
Cited alongside, same era.
Later among the works it cites.
A. Kwong, D. Genkin, D. Gruss, and Y. Yarom, “Rambleed: Reading bits in memory without accessing them,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Rolnick and K. Kording, “Reverse-engineering deep relu networks,” in
2020
Later among the works it cites.
M. H. I. Chowdhuryy, H. Liu, and F. Yao, “Branchspec: Information leakage attacks exploiting speculative branch instruction executions,” in
2020
Later among the works it cites.
X. Hu, L. Liang, S. Li, L. Deng, P. Zuo, Y. Ji, X. Xie, Y. Ding, C. Liu, T. Sherwood
2020
Later among the works it cites.
F. Yao, A. S. Rakin, and D. Fan, “Deephammer: Depleting the intelligence of deep neural networks through targeted chain of bit flips,” in
2020
Later among the works it cites.
S. Addepalli, G. K. Nayak, A. Chakraborty, and V. B. Radhakrishnan, “Degan: Data-enriching gan for retrieving representative samples from a trained classifier,” in
2020
Later among the works it cites.
H. Yu, H. Ma, K. Yang, Y. Zhao, and Y. Jin, “Deepem: Deep neural networks model recovery through em side-channel information leakage,” in
2020
Later among the works it cites.
J. Wei, Y. Zhang, Z. Zhou, Z. Li, and M. A. Al Faruque, “Leaky dnn: Stealing deep-learning model secret with gpu context-switching side-channel,” in
2020
Later among the works it cites.
M. Yan, C. W. Fletcher, and J. Torrellas, “Cache telepathy: Leveraging shared resource attacks to learn DNN architectures,” in
2020
Later among the works it cites.
Y. Xiang, Z. Chen, Z. Chen, Z. Fang, H. Hao, J. Chen, Y. Liu, Z. Wu, Q. Xuan, and X. Yang, “Open dnn box by power side-channel attack,”
2020
Later among the works it cites.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in
2020
Later among the works it cites.
W. Cui, X. Li, J. Huang, W. Wang, S. Wang, and J. Chen, “Substitute model generation for black-box adversarial attack based on knowledge distillation,” in
2020
Later among the works it cites.
M. H. I. Chowdhuryy and F. Yao, “Leaking secrets through modern branch predictors in the speculative world,”
2021
Closest in time.
Y. Zhang, R. Yasaei, H. Chen, Z. Li, and M. A. Al Faruque, “Stealing neural network structure through remote fpga side-channel analysis,” in
2021
Closest in time.
Z. Zhang, S. Liang, F. Yao, and X. Gao, “Red alert for power leakage: Exploiting intel rapl-induced side channels,” in
2021
Closest in time.
K. Cai, M. H. I. Chowdhuryy, Z. Zhang, and F. Yao, “Seeds of seed: Nmt-stroke: Diverting neural machine translation through hardware-based faults,” 2021
2021
Closest in time.