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Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs.
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Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: ACM AsiACCS (2017)
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Ducoffe, M., Precioso, F.: Adversarial active learning for deep networks: a margin based approach. In: ICML (2018)
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)
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Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach. In: ICLR (2018)
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Orekondy, T., Schiele, B., Fritz, M.: Knockoff nets: Stealing functionality of black-box models. In: CVPR (2019)
2019
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Orekondy, T., Schiele, B., Fritz, M.: Prediction poisoning: Towards defenses against dnn model stealing attacks. In: ICLR (2019)
2019
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Barbalau, A., Cosma, A., Ionescu, R.T., Popescu, M.: Black-box ripper: Copying black-box models using generative evolutionary algorithms. In: NeurIPS (2020)
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2020
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Yu, H., Yang, K., Zhang, T., Tsai, Y.Y., Ho, T.Y., Jin, Y.: Cloudleak: Large-scale deep learning models stealing through adversarial examples. In: NDSS (2020)
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Zhou, M., Wu, J., Liu, Y., Liu, S., Zhu, C.: Dast: Data-free substitute training for adversarial attacks. In: CVPR (2020)
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Dong, X., Luu, A.T., Ji, R., Liu, H.: Towards robustness against natural language word substitutions. In: ICLR (2021)
2021
Closest in time.
Gong, X., Chen, Y., Yang, W., Mei, G., Wang, Q.: Inversenet: Augmenting model extraction attacks with training data inversion. In: IJCAI (2021)
2021
Closest in time.
Maini, P., Yaghini, M., Papernot, N.: Dataset inference: Ownership resolution in machine learning. In: ICLR (2021)
2021
Closest in time.
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2021
Closest in time.
Fang, S., Li, J., Lin, X., Ji, R.: Learning to learn transferable attack. In: AAAI (2022)
2022
Closest in time.