SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning

H. Jia, C. A. Choquette-Choo, V. Chandrasekaran, and N. Papernot, “Entangled watermarks as a defense against model extraction,” in USENIX Security Symposium , 2021, pp. 1937–1954

1954

Earlier work this paper cites.

M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” in Proceedings of the 2000 ACM SIGMOD international conference on Management of data , 2000, pp. 93–104

2000

Earlier work this paper cites.

G. E. Hinton and S. Roweis, “Stochastic neighbor embedding,” NeurIPS , 2002

2002

Earlier work this paper cites.

J. P. Crutchfield and D. P. Feldman, “Regularities unseen, randomness observed: Levels of entropy convergence,” Chaos: An Interdisciplinary Journal of Nonlinear Science , vol. 13, no. 1, pp. 25–54, 2003

2003

Earlier work this paper cites.

B. Dolan and C. Brockett, “Automatically constructing a corpus of sentential paraphrases,” in IWP , 2005

2005

Earlier work this paper cites.

R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in CVPR . IEEE, 2006

2006

Earlier work this paper cites.

A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009

2009

Earlier work this paper cites.

A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in AISTATS . JMLR Workshop and Conference Proceedings, 2011

2011

Earlier work this paper cites.

A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies , 2011, pp. 142–150

2011

Earlier work this paper cites.

J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The german traffic sign recognition benchmark: a multi-class classification competition,” in IJCNN . IEEE, 2011

2011

Earlier work this paper cites.

Y. Wu, Y. Zhou, G. Saveriades, S. Agaian, J. P. Noonan, and P. Natarajan, “Local shannon entropy measure with statistical tests for image randomness,” Information Sciences , vol. 222, pp. 323–342, 2013

2013

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

Original

2014

Earlier work this paper cites.

S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,” in EMNLP . Association for Computational Linguistics, 2015

2015

Earlier work this paper cites.

A. M. Dai and Q. V. Le, “Semi-supervised sequence learning,” NeurIPS , vol. 28, 2015

2015

Earlier work this paper cites.

S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” NeurIPS , vol. 28, 2015

2015

Earlier work this paper cites.

J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” arXiv preprint arXiv:1605.09782 , 2016

Original

2016

Earlier work this paper cites.

V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville, “Adversarially learned inference,” arXiv preprint arXiv:1606.00704 , 2016

Original

2016

Earlier work this paper cites.

A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in KDD , 2016

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016

2016

Earlier work this paper cites.

G. Larsson, M. Maire, and G. Shakhnarovich, “Learning representations for automatic colorization,” in European conference on computer vision . Springer, 2016, pp. 577–593

2016

Earlier work this paper cites.

S. Merity, C. Xiong, J. Bradbury, and R. Socher, “Pointer sentinel mixture models,” arXiv preprint arXiv:1609.07843 , 2016

Original

2016

Earlier work this paper cites.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in CVPR , 2016, pp. 2536–2544

2016

Earlier work this paper cites.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in CVPR , 2016

2016

Earlier work this paper cites.

A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves et al. , “Conditional image generation with pixelcnn decoders,” NeurIPS , 2016

2016

Earlier work this paper cites.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR , 2017

2017

Cited alongside, same era.

S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto, “Unified deep supervised domain adaptation and generalization,” in ICCV , 2017

2017

Cited alongside, same era.

Y. Uchida, Y. Nagai, S. Sakazawa, and S. Satoh, “Embedding watermarks into deep neural networks,” in ICMR , 2017

2017

Cited alongside, same era.

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 , 2018, pp. 1615–1631

2018

Cited alongside, same era.

L. N. Darlow, E. J. Crowley, A. Antoniou, and A. J. Storkey, “Cinic-10 is not imagenet or cifar-10,” arXiv preprint arXiv:1810.03505 , 2018

K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR , 2020

2020

Later among the works it cites.

M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy, “Spanbert: Improving pre-training by representing and predicting spans,” Transactions of the Association for Computational Linguistics , vol. 8, pp. 64–77, 2020

2020

Later among the works it cites.

R. Pang, Z. Zhang, X. Gao, Z. Xi, S. Ji, P. Cheng, and T. Wang, “Trojanzoo: Everything you ever wanted to know about neural backdoors (but were afraid to ask),” arXiv preprint arXiv:2012.09302 , 2020

Original

2020

Later among the works it cites.

K. Wiggers, GPT-3 Cost , https://venturebeat.com/2020/06/01/ai-machine-learning-openai-gpt-3-size-isnt-everything/, 2020

2020

Later among the works it cites.

E. Bagdasaryan and V. Shmatikov, “Blind backdoors in deep learning models,” in USENIX Security , 2021