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Perturbative availability poisons (PAPs) add small changes to images to prevent their use for model training.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Deep learning in neural networks: An overview
Schmidhuber, J · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Understanding how image quality affects deep neural networks
Dodge, S. and Karam, L · 2016
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A study of the effect of jpg compression on adversarial images
Dziugaite, G. K., Ghahramani, Z., and Roy, D. M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Faceless person recognition: Privacy implications in social media
Oh, S. J., Benenson, R., Fritz, M., and Schiele, B · 2016
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Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
Das, N., Shanbhogue, M., Chen, S.-T., Hohman, F., Chen, L., Kounavis, M. E., and Chau, D. H · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Adversarial image perturbation for privacy protection a game theory perspective
Oh, S. J., Fritz, M., and Schiele, B · 2017
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Towards a visual privacy advisor: Understanding and predicting privacy risks in images
Orekondy, T., Schiele, B., and Fritz, M · 2017
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Jpeg-resistant adversarial images
Shin, R. and Song, D · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Pixel privacy. increasing image appeal while blocking automatic inference of sensitive scene information
Larson, M., Liu, Z., Brugman, S., and Zhao, Z · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Detecting adversarial examples through image transformation
Tian, S., Yang, G., and Cai, Y · 2018
Cited alongside, same era.
Mitigating adversarial effects through randomization
Xie, C., Wang, J., Zhang, Z., Ren, Z., and Yuille, A · 2018
Cited alongside, same era.
Pre-training on grayscale imagenet improves medical image classification
Xie, Y. and Richmond, D · 2018
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On evaluating adversarial robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I., Madry, A., and Kurakin, A · 2019
Cited alongside, same era.
Evading defenses to transferable adversarial examples by translation-invariant attacks
Dong, Y., Pang, T., Su, H., and Zhu, J · 2019
Cited alongside, same era.
Learning to confuse: generating training time adversarial data with auto-encoder
On adaptive attacks to adversarial example defenses
Tramer, F., Carlini, N., Brendel, W., and Madry, A · 2020
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Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition
Cherepanova, V., Goldblum, M., Foley, H., Duan, S., Dickerson, J., Taylor, G., and Goldstein, T · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2021
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Disrupting model training with adversarial shortcuts
Evtimov, I., Covert, I., Kusupati, A., and Kohno, T · 2021
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Robust unlearnable examples: Protecting data privacy against adversarial learning
Fu, S., He, F., Liu, Y., Shen, L., and Tao, D · 2021
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Feng, J., Cai, Q.-Z., and Zhou, Z.-H · 2019
Cited alongside, same era.
Low frequency adversarial perturbation
Guo, C., Frank, J. S., and Weinberger, K. Q · 2019
Cited alongside, same era.
Scene privacy protection
Li, C. Y., Shamsabadi, A. S., Sanchez-Matilla, R., Mazzon, R., and Cavallaro, A · 2019
Cited alongside, same era.
Cross-domain transferability of adversarial perturbations
Naseer, M. M., Khan, S. H., Khan, M. H., Shahbaz Khan, F., and Porikli, F · 2019
Cited alongside, same era.
On the spectral bias of neural networks
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., and Courville, A · 2019
Cited alongside, same era.
TensorClog: An imperceptible poisoning attack on deep neural network applications
Shen, J., Zhu, X., and Ma, D · 2019
Cited alongside, same era.
Adversarial training and robustness for multiple perturbations
Tramer, F. and Boneh, D · 2019
Cited alongside, same era.
Huang, H., Ma, X., Erfani, S. M., Bailey, J., and Wang, Y · 2021
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Perceptual adversarial robustness: Defense against unseen threat models
Laidlaw, C., Singla, S., and Feizi, S · 2021
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Theory of the frequency principle for general deep neural networks
Luo, T., Ma, Z., Xu, Z.-Q. J., and Zhang, Y · 2021
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On the (im) practicality of adversarial perturbation for image privacy
Rajabi, A., Bobba, R. B., Rosulek, M., Wright, C., and Feng, W.-c · 2021
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Better safe than sorry: Preventing delusive adversaries with adversarial training
Tao, L., Feng, L., Yi, J., Huang, S.-J., and Chen, S · 2021
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Fooling adversarial training with inducing noise
Wang, Z., Wang, Y., and Wang, Y · 2021
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Neural tangent generalization attacks
Yuan, C.-H. and Wu, S.-H · 2021
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Data poisoning won’t save you from facial recognition
Radiya-Dixit, E., Hong, S., Carlini, N., and Tramèr, F · 2022
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Autoregressive perturbations for data poisoning
Sandoval-Segura, P., Singla, V., Geiping, J., Goldblum, M., Goldstein, T., and Jacobs, D. W · 2022
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Generative poisoning using random discriminators
van Vlijmen, D., Kolmus, A., Liu, Z., Zhao, Z., and Larson, M · 2022
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Availability attacks create shortcuts
Yu, D., Zhang, H., Chen, W., Yin, J., and Liu, T.-Y · 2022
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Self-ensemble protection: Training checkpoints are good data protectors
Chen, S., Yuan, G., Cheng, X., Gong, Y., Qin, M., Wang, Y., and Huang, X · 2023
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Indiscriminate poisoning attacks on unsupervised contrastive learning
He, H., Zha, K., and Katabi, D · 2023
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Transferable unlearnable examples
Ren, J., Xu, H., Wan, Y., Ma, X., Sun, L., and Tang, J · 2023
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Is adversarial training really a silver bullet for mitigating data poisoning?
Wen, R., Zhao, Z., Liu, Z., Backes, M., Wang, T., and Zhang, Y · 2023
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One-pixel shortcut: on the learning preference of deep neural networks
Wu, S., Chen, S., Xie, C., and Huang, X · 2023
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