[1] Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song. The secret revealer: Generative model-inversion attacks against deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 253-261, 2020.
[2] Si Chen, Mostafa Kahla, Ruoxi Jia, and Guo-Jun Qi. Knowledge-enriched distributional model inversion attacks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 16178-16187, 2021.
[3] Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani. Variational model inversion attacks. Advances in Neural Information Processing Systems, 34:9706-9719, 2021.
[4] Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang. Neural network inversion in adversarial setting via background knowledge alignment. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 225-240, 2019.
[5] Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, and Kristian Kersting. Plug and play attacks: Towards robust and flexible model inversion attacks. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 20522-20545. PMLR, 17-23 Jul 2022.
[6] Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. Label-only model inversion attacks via boundary repulsion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15045-15053, 2022.
[7] Xiaojian Yuan, Kejiang Chen, Jie Zhang, Weiming Zhang, Nenghai Yu, and Yang Zhang. Pseudo label-guided model inversion attack via conditional generative adversarial network. Thirty Seventh AAAI Conference on Artificial Intelligence (AAAI 23), 2023.
[8] Mahdi Khosravy, Kazuaki Nakamura, Yuki Hirose, Naoko Nitta, and Noboru Babaguchi. Model inversion attack by integration of deep generative models: Privacy-sensitive face generation from a face recognition system. IEEE Transactions on Information Forensics and Security, 17:357-372, 2022.
[9] Tianhao Wang, Yuheng Zhang, and Ruoxi Jia. Improving robustness to model inversion attacks via mutual information regularization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11666-11673, 2021.
[10] Shengwei An, Guanhong Tao, Qiuling Xu, Yingqi Liu, Guangyu Shen, Yuan Yao, Jingwei Xu, and Xiangyu Zhang. Mirror: Model inversion for deep learning network with high fidelity. In Proceedings of the 29th Network and Distributed System Security Symposium, 2022.
[11] Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and Ngai-Man Cheung. Rethinking model inversion attacks against deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16384-16393, June 2023.
[12] Ulrich Aivodji, Sebastien Gambs, and Timon Ther. Gamin: An adversarial approach to black-box model inversion. In The AAAI Workshop on Privacy-Preserving Artificial Intelligence, 2020.
[13] Gyojin Han, Jaehyun Choi, Haeil Lee, and Junmo Kim. Reinforcement learning-based black-box model inversion attacks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20504-20513, June 2023.
[14] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, 2014.