Copyright Protection Studies in Deep Learning

Here's an ongoing list to summarize literature about copyright protection in deep learning, including the copyright of models and data.

And we would greatly appreciate your contributions to expand this list! ✧٩(^ω^)و✧

Year	Title	Copyright Subject	Task Type	Method	Authors	Publisher	🔗
2019	BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks	Model	Image Encoding	Watermarking	Huili Chen, Bita Darvish Rouhani, and Farinaz Koushanfar	ArXiv	pdf
2020	Membership Encoding for Deep Learning	Model	Classification	Membership Inference	Congzheng Song, and Reza Shokri	AsiaCCS	pdf
2020	Towards Probabilistic Verification of Machine Unlearning	Data	Classification	Dirty-label Backdoor Attack	David M. Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal	ArXiv	pdf
2021	Deep Neural Network Fingerprinting by Conferrable Adversarial Examples	Model	Classification	Adversarial Training; Transfer Learning	Nils Lukas, Yuxuan Zhang, and Florian Kerschbaum	ICLR	pdf & code
2022	Defending against Model Stealing via Verifying Embedded External Features	Model	Classification	Dirty-label Backdoor Attack; Hypothesis Testing	Li Yiming, Zhu Linghui, Jia Xiaojun, Jiang Yong, Xia Shu-Tao, and Cao Xiaochun	AAAI	pdf & code
2022	Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication	Model & Data	Classification	Membership Inference	Gaoyang Liu, Tianlong Xu, Xiaoqiang Ma, and Chen Wang	TIFS	pdf
2022	Deep Model Intellectual Property Protection via Deep Watermarking	Model	Classification	Watermarking; Steganography	Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, and Nenghai Yu	TPAMI	pdf & code
2022	Data Isotopes for Data Provenance in DNNs	Data	Classification	Watermarking	Emily Wenger, Xiuyu Li, Ben Y. Zhao, and Vitaly Shmatikov	Arxiv	pdf
2022	Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models	Model	Classification	Testing	Jialuo Chen, Jingyi Wang, Tinglan Peng, Youcheng Sun, Peng Cheng, Shouling Ji, Xingjun Ma, Bo Li, and Dawn Song	S&P	pdf & code
2022	Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization	Model	Classification	Adversarial Training; Domain Shift;	Lixu Wang, Shichao Xu, Ruiqi Xu, Xiao Wang, and Qi Zhu	ICLR	pdf & code
2022	Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection	Dataset	Classification	Dirty-&Clean-label Backdoor Attack; Adversarial Training; Hypothesis Testing	Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, and Bo Li	NeurIPS	pdf & code
2023	Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking	Dataset	Classification	Clean-label Backdoor Attack; Hypothesis Testing	Ruixiang Tang, Qizhang Feng, Ninghao Liu, Fan Yang, and Xia Hu	arXiv	pdf & code
2023	Black-Box Dataset Ownership Verification via Backdoor Watermarking	Dataset	Classification	Dirty-label Backdoor Attack; Hypothesis Testing	Yiming Li, Mingyan Zhu, Xue Yang, Yong Jiang, Tao Wei, and Shu-Tao Xia	TIFS	pdf & code
2023	Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand	Dataset	Classification	Adversarial Training; Domain Shift; Hypothesis Testing	Junfeng Guo, Yiming Li, Lixu Wang, Shu-Tao Xia, Heng Huang, Cong Liu, and Bo Li	NeurIPS	pdf & code

What's the difference between Ownership and Copyright in deep learning?