Fetching the paper…
Reading the bibliography…
While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks.
C. A. C. Choo, F. Tramèr, N. Carlini, and N. Papernot, “Label-Only Membership Inference Attacks,” in International Conference on Machine Learning (ICML) . PMLR, 2021, pp. 1964–1974
1974
Earlier work this paper cites.
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig, “Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays,” PLOS Genetics , 2008
2008
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. Dahl, A. rahman Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine , 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Annual Conference on Neural Information Processing Systems (NIPS) . NIPS, 2012, pp. 1106–1114
2012
Earlier work this paper cites.
A. Graves, A. rahman Mohamed, and G. E. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2013, pp. 6645–6649
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Nets,” in Annual Conference on Neural Information Processing Systems (NIPS) . NIPS, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing Properties of Neural Networks,” in International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2015, pp. 1322–1333
2015
Earlier work this paper cites.
G. Levi and T. Hassner, “Age and Gender Classification using Convolutional Neural Networks,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, 2015, pp. 34–42
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep Learning Face Attributes in the Wild,” in IEEE International Conference on Computer Vision (ICCV) . IEEE, 2015, pp. 3730–3738
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and Tell: A Neural Image Caption Generator,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2015, pp. 3156–3164
2015
Earlier work this paper cites.
M. Backes, P. Berrang, M. Humbert, and P. Manoharan, “Membership Privacy in MicroRNA-based Studies,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2016, pp. 319–330
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2016, pp. 770–778
2016
Earlier work this paper cites.
Z. Niu, M. Zhou, L. Wang, X. Gao, and G. Hua, “Ordinal Regression with Multiple Output CNN for Age Estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2016, pp. 4920–4928
2016
Earlier work this paper cites.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing Machine Learning Models via Prediction APIs,” in USENIX Security Symposium (USENIX Security) . USENIX, 2016, pp. 601–618
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein Generative Adversarial Networks,” in International Conference on Machine Learning (ICML) . PMLR, 2017, pp. 214–223
2017
Earlier work this paper cites.
S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang, “Generalization and Equilibrium in Generative Adversarial Nets (GANs),” in International Conference on Machine Learning (ICML) . PMLR, 2017, pp. 224–232
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved Training of Wasserstein GANs,” in Annual Conference on Neural Information Processing Systems (NIPS) . NIPS, 2017, pp. 5767–5777
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium,” in Annual Conference on Neural Information Processing Systems (NIPS) . NIPS, 2017, pp. 6626–6637
2017
Cited alongside, same era.
S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and Locally Consistent Image Completion,” ACM Transactions on Graphics , 2017
2017
Cited alongside, same era.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised Image-to-Image Translation Networks,” in Annual Conference on Neural Information Processing Systems (NIPS) . NIPS, 2017, pp. 700–708
2017
Cited alongside, same era.
S. Mehri, K. Kumar, I. Gulrajani, R. Kumar, S. Jain, J. Sotelo, A. C. Courville, and Y. Bengio, “SampleRNN: An Unconditional End-to-End Neural Audio Generation Model,” in International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
L. Melis, C. Song, E. D. Cristofaro, and V. Shmatikov, “Exploiting Unintended Feature Leakage in Collaborative Learning,” in IEEE Symposium on Security and Privacy (S&P) . IEEE, 2019, pp. 497–512
2019
Later among the works it cites.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning,” in IEEE Symposium on Security and Privacy (S&P) . IEEE, 2019, pp. 1021–1035
2019
Later among the works it cites.
T. Orekondy, B. Schiele, and M. Fritz, “Knockoff Nets: Stealing Functionality of Black-Box Models,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2019, pp. 4954–4963
2019
Later among the works it cites.
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, “ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models,” in Network and Distributed System Security Symposium (NDSS) . Internet Society, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Papernot, P. D. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical Black-Box Attacks Against Machine Learning,” in ACM Asia Conference on Computer and Communications Security (ASIACCS) . ACM, 2017, pp. 506–519
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks Against Machine Learning Models,” in IEEE Symposium on Security and Privacy (S&P) . IEEE, 2017, pp. 3–18
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” in Conference on Fairness, Accountability, and Transparency (FAT*) . PMLR, 2018, pp. 77–91
2018
Cited alongside, same era.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust Physical-World Attacks on Deep Learning Visual Classification,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2018, pp. 1625–1634
2018
Cited alongside, same era.
K. Ganju, Q. Wang, W. Yang, C. A. Gunter, and N. Borisov, “Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2018, pp. 619–633
2018
Cited alongside, same era.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning Attack on Neural Networks,” in Network and Distributed System Security Symposium (NDSS) . Internet Society, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
C. Song and V. Shmatikov, “Auditing Data Provenance in Text-Generation Models,” in ACM Conference on Knowledge Discovery and Data Mining (KDD) . ACM, 2019, pp. 196–206
2019
Later among the works it cites.
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks,” in IEEE Symposium on Security and Privacy (S&P) . IEEE, 2019, pp. 707–723
2019
Later among the works it cites.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2020, pp. 343–362
2020
Later among the works it cites.
M. Jagielski, N. Carlini, D. Berthelot, A. Kurakin, and N. Papernot, “High Accuracy and High Fidelity Extraction of Neural Networks,” in USENIX Security Symposium (USENIX Security) . USENIX, 2020, pp. 1345–1362
2020
Later among the works it cites.
K. Leino and M. Fredrikson, “Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference,” in USENIX Security Symposium (USENIX Security) . USENIX, 2020, pp. 1605–1622
2020
Later among the works it cites.
A. Salem, A. Bhattacharya, M. Backes, M. Fritz, and Y. Zhang, “Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning,” in USENIX Security Symposium (USENIX Security) . USENIX, 2020, pp. 1291–1308
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Yu, K. Yang, T. Zhang, Y.-Y. Tsai, T.-Y. Ho, and Y. Jin, “CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples,” in Network and Distributed System Security Symposium (NDSS) . Internet Society, 2020
2020
Later among the works it cites.
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang, “When Machine Unlearning Jeopardizes Privacy,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2021
2021
Closest in time.
X. Chen, A. Salem, M. Backes, S. Ma, Q. Shen, Z. Wu, and Y. Zhang, “BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements,” in Annual Computer Security Applications Conference (ACSAC) . ACSAC, 2021
2021
Closest in time.
X. He, J. Jia, M. Backes, N. Z. Gong, and Y. Zhang, “Stealing Links from Graph Neural Networks,” in USENIX Security Symposium (USENIX Security) . USENIX, 2021, pp. 2669–2686
2021
Closest in time.
2021
Closest in time.
X. He and Y. Zhang, “Quantifying and Mitigating Privacy Risks of Contrastive Learning,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2021
2021
Closest in time.
Z. Li and Y. Zhang, “Membership Leakage in Label-Only Exposures,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2021
2021
Closest in time.
H. Liu, J. Jia, W. Qu, and N. Z. Gong, “EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2021
2021
Closest in time.
2021
Closest in time.
M. Zhang, Z. Ren, Z. Wang, P. Ren, Z. Chen, P. Hu, and Y. Zhang, “Membership Inference Attacks Against Recommender Systems,” in ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2021
2021
Closest in time.
Y. Liu, R. Wen, X. He, A. Salem, Z. Zhang, M. Backes, E. D. Cristofaro, M. Fritz, and Y. Zhang, “ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models,” in USENIX Security Symposium (USENIX Security) . USENIX, 2022
2022
Closest in time.