Chen, Jialuo, Jingyi, Wang, Peng, Tinglan, Sun, Youcheng, Cheng, Peng, Ji, Shouling, Ma, Xingjun, Li, Bo, and Song, Dawn. Copy, right? A testing framework for copyright protection of deep learning models. Retrieved from https://par.nsf.gov/biblio/10347546. Proceedings of the IEEE Symposium on Security and Privacy . Web. doi:10.1109/SP46214.2022.9833747.

Chen, Jialuo, Jingyi, Wang, Peng, Tinglan, Sun, Youcheng, Cheng, Peng, Ji, Shouling, Ma, Xingjun, Li, Bo, & Song, Dawn. Copy, right? A testing framework for copyright protection of deep learning models. Proceedings of the IEEE Symposium on Security and Privacy, (). Retrieved from https://par.nsf.gov/biblio/10347546. https://doi.org/10.1109/SP46214.2022.9833747

Chen, Jialuo, Jingyi, Wang, Peng, Tinglan, Sun, Youcheng, Cheng, Peng, Ji, Shouling, Ma, Xingjun, Li, Bo, and Song, Dawn. "Copy, right? A testing framework for copyright protection of deep learning models". Proceedings of the IEEE Symposium on Security and Privacy (). Country unknown/Code not available. https://doi.org/10.1109/SP46214.2022.9833747. https://par.nsf.gov/biblio/10347546.

@article{osti_10347546,
place = {Country unknown/Code not available}, title = {Copy, right? A testing framework for copyright protection of deep learning models}, url = {https://par.nsf.gov/biblio/10347546}, DOI = {10.1109/SP46214.2022.9833747}, abstractNote = {}, journal = {Proceedings of the IEEE Symposium on Security and Privacy}, author = {Chen, Jialuo and Jingyi, Wang and Peng, Tinglan and Sun, Youcheng and Cheng, Peng and Ji, Shouling and Ma, Xingjun and Li, Bo and Song, Dawn}, }