Fetching the paper…
Reading the bibliography…
Deep Neural Networks (DNN) are gaining higher commercial values in computer vision applications, e.g., image classification, video analytics, etc.
2008
Earlier work this paper cites.
L. Luo, Z. Chen, M. Chen, X. Zeng, and Z. Xiong, “Reversible image watermarking using interpolation technique,” IEEE Transactions on Information Forensics and Security , vol. 5, no. 1, pp. 187–193, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems , vol. 25, pp. 1097–1105, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
F. McKeen, I. Alexandrovich, A. Berenzon, C. V. Rozas, H. Shafi, V. Shanbhogue, and U. R. Savagaonkar, “Innovative instructions and software model for isolated execution.” Hasp@ isca , vol. 10, no. 1, 2013
2013
Earlier work this paper cites.
Y. Yarom and K. Falkner, “FLUSH+ RELOAD: A high resolution, low noise, L3 cache side-channel attack,” in USENIX Security Symposium , 2014
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 IEEE S&P , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Kaplan, J. Powell, and T. Woller, “Amd memory encryption,” 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Uchida, Y. Nagai, S. Sakazawa, and S. Satoh, “Embedding watermarks into deep neural networks,” in ACM on International Conference on Multimedia Retrieval , 2017, pp. 269–277
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Schwarz, S. Weiser, D. Gruss, C. Maurice, and S. Mangard, “Malware guard extension: Using SGX to conceal cache attacks,” in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment . Springer, 2017, pp. 3–24
2017
Earlier work this paper cites.
F. Brasser, U. Müller, A. Dmitrienko, K. Kostiainen, S. Capkun, and A.-R. Sadeghi, “Software grand exposure: SGX cache attacks are practical,” in USENIX Workshop on Offensive Technologies , 2017
2017
Earlier work this paper cites.
J. Götzfried, M. Eckert, S. Schinzel, and T. Müller, “Cache attacks on Intel SGX,” in European Workshop on Systems Security , 2017, pp. 1–6
2017
Earlier work this paper cites.
M. Hähnel, W. Cui, and M. Peinado, “High-resolution side channels for untrusted operating systems,” in USENIX ATC , 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Adi, C. Baum, M. Cisse, B. Pinkas, and J. Keshet, “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” in USENIX Security Symposium , 2018, pp. 1615–1631
2018
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in IEEE CVPR , 2018
2018
Earlier work this paper cites.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” arXiv preprint , 2018
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
G. Bender, P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le, “Understanding and simplifying one-shot architecture search,” in International Conference on Machine Learning , 2018, pp. 550–559
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. Nelson, A. Bridgland et al. , “Improved protein structure prediction using potentials from deep learning,” Nature , vol. 577, no. 7792, pp. 706–710, 2020
2020
Later among the works it cites.
H. Fang, D. Chen, Q. Huang, J. Zhang, Z. Ma, W. Zhang, and N. Yu, “Deep template-based watermarking,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 4, pp. 1436–1451, 2020
2020
Later among the works it cites.
J. Zhang, D. Chen, J. Liao, H. Fang, W. Zhang, W. Zhou, H. Cui, and N. Yu, “Model watermarking for image processing networks,” in AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 805–12 812
2020
Later among the works it cites.
H. Wu, G. Liu, Y. Yao, and X. Zhang, “Watermarking neural networks with watermarked images,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 7, pp. 2591–2601, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 IEEE Symposium on Security and Privacy , 2018, pp. 245–261
2018
Cited alongside, same era.
M. Marschalek, “The wolf in SGX clothing,” in Bluehat IL , 2018
2018
Cited alongside, same era.
J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting intellectual property of deep neural networks with watermarking,” in ACM AsiaCCS , 2018
2018
Cited alongside, same era.
V. Duddu, D. Samanta, D. V. Rao, and V. E. Balas, “Stealing neural networks via timing side channels,” arXiv preprint , 2018
2018
Cited alongside, same era.
W. Hua, Z. Zhang, and G. E. Suh, “Reverse engineering convolutional neural networks through side-channel information leaks,” in ACM/ESDA/IEEE Design Automation Conference . IEEE, 2018, pp. 1–6
2018
Cited alongside, same era.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive neural architecture search,” in European Conference on Computer Vision , 2018, pp. 19–34
2018
Cited alongside, same era.
A. Roy and R. S. Chakraborty, “Toward optimal prediction error expansion-based reversible image watermarking,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 8, pp. 2377–2390, 2019
2019
Cited alongside, same era.
B. D. Rouhani, H. Chen, and F. Koushanfar, “DeepSigns: An end-to-end watermarking framework for protecting the ownership of deep neural networks,” in ACM ASPLOS , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
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 USENIX Security Symposium , 2020, pp. 2003–2020
2020
Later among the works it cites.
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 et al. , “DeepSniffer: A DNN model extraction framework based on learning architectural hints,” in Architectural Support for Programming Languages and Operating Systems , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Chu, T. Zhou, B. Zhang, and J. Li, “Fair DARTS: Eliminating unfair advantages in differentiable architecture search,” in European Conference on Computer Vision , 2020, pp. 465–480
2020
Later among the works it cites.
S. Guo, T. Zhang, G. Xu, H. Yu, T. Xiang, and Y. Liu, “Topology-aware differential privacy for decentralized image classification,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
Y. Wang, X. Fan, R. Xiong, D. Zhao, and W. Gao, “Neural network-based enhancement to inter prediction for video coding,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 2, pp. 826–838, 2021
2021
Closest in time.
Q. Li, X. Wang, B. Ma, X. Wang, C. Wang, S. Gao, and Y. Shi, “Concealed attack for robust watermarking based on generative model and perceptual loss,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
L. Xiong, X. Han, C.-N. Yang, and Y.-Q. Shi, “Robust reversible watermarking in encrypted image with secure multi-party based on lightweight cryptography,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 1, pp. 75–91, 2021
2021
Closest in time.
J. You, Y.-G. Wang, G. Zhu, and S. Kwong, “Truncated robust natural watermarking with hungarian optimization,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
F. Peng, B. Long, and M. Long, “A general region nesting-based semi-fragile reversible watermarking for authenticating 3d mesh models,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 11, pp. 4538–4553, 2021
2021
Closest in time.
K. Chen, S. Guo, T. Zhang, S. Li, and Y. Liu, “Temporal watermarks for deep reinforcement learning models,” in International Conference on Autonomous Agents and Multiagent Systems , 2021
2021
Closest in time.
X. Chen, W. Wang, C. Bender, Y. Ding, R. Jia, B. Li, and D. Song, “REFIT: A unified watermark removal framework for deep learning systems with limited data,” ACM AsiaCCS , 2021
2021
Closest in time.
S. Guo, T. Zhang, H. Qiu, Y. Zeng, T. Xiang, and Y. Liu, “Fine-tuning is not enough: A simple yet effective watermark removal attack for DNN models,” International Joint Conference on Artificial Intelligence , 2021
2021
Closest in time.
L. Li, Y. Zhang, S. Tang, L. Xie, X. Li, and Q. Tian, “Adaptive spatial location with balanced loss for video captioning,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 1, pp. 17–30, 2022
2022
Closest in time.
L. Zhou, X. Ding, and F. Zhang, “Smile: Secure memory introspection for live enclave,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, 2022, pp. 1536–1536
2022
Closest in time.