{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T06:43:26Z","timestamp":1760424206958,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819531844","type":"print"},{"value":"9789819531851","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3185-1_15","type":"book-chapter","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T06:05:52Z","timestamp":1760421952000},"page":"221-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bypassing Cross-Domain Restrictions with\u00a0Unsupervised Visual Translation"],"prefix":"10.1007","author":[{"given":"Huali","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyu","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weitong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiachao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong-zhi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"15_CR1","unstructured":"Adi, Y., Baum, C., Cisse, M., Pinkas, B., Keshet, J.: Turning your weakness into a strength: watermarking deep neural networks by backdooring. In: 27th USENIX Security Symposium (USENIX Security 18), pp. 1615\u20131631 (2018)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Cao, X., Jia, J., Gong, N.Z.: IPGuard: protecting intellectual property of deep neural networks via fingerprinting the classification boundary. In: Proceedings of the 2021 ACM ASIA Conference on Computer and Communications Security, pp. 14\u201325 (2021)","DOI":"10.1145\/3433210.3437526"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Copy, right? A testing framework for copyright protection of deep learning models. In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 824\u2013841. IEEE (2022)","DOI":"10.1109\/SP46214.2022.9833747"},{"key":"15_CR4","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 215\u2013223. JMLR Workshop and Conference Proceedings (2011)"},{"issue":"6","key":"15_CR5","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Ding, R., Su, L., Ding, A.A., Fei, Y.: Non-transferable pruning. In: European Conference on Computer Vision, pp. 375\u2013393. Springer (2024)","DOI":"10.1007\/978-3-031-73016-0_22"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Hong, Z., Shen, L., Liu, T.: Your transferability barrier is fragile: free-lunch for transferring the non-transferable learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 28805\u201328815 (2024)","DOI":"10.1109\/CVPR52733.2024.02721"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Hong, Z., Xiang, Y., Liu, T.: Toward robust non-transferable learning: A survey and benchmark. arXiv preprint arXiv:2502.13593 (2025)","DOI":"10.24963\/ijcai.2025\/1161"},{"issue":"5","key":"15_CR9","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull, J.J.: A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 550\u2013554 (1994)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"15_CR11","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Li, J., Yu, Z., Du, Z., Zhu, L., Shen, H.T.: A comprehensive survey on source-free domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","DOI":"10.1109\/TPAMI.2024.3370978"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Liu, K., Dolan-Gavitt, B., Garg, S.: Fine-pruning: defending against backdooring attacks on deep neural networks. In: International Symposium on Research in Attacks, Intrusions, and Defenses, pp. 273\u2013294. Springer (2018)","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Lukas, N., Jiang, E., Li, X., Kerschbaum, F.: SoK: how robust is image classification deep neural network watermarking? In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 787\u2013804. IEEE (2022)","DOI":"10.1109\/SP46214.2022.9833693"},{"issue":"4","key":"15_CR15","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TITS.2017.2714691","volume":"19","author":"H Luo","year":"2017","unstructured":"Luo, H., Yang, Y., Tong, B., Wu, F., Fan, B.: Traffic sign recognition using a multi-task convolutional neural network. IEEE Trans. Intell. Transp. Syst. 19(4), 1100\u20131111 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"15_CR16","unstructured":"Neil, H., Dirk, W.: Transformers for image recognition at scale. Online: https:\/\/aigoogleblog.com\/2020\/12\/transformers-forimage-recognitionat.html (2020)"},{"key":"15_CR17","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y., et\u00a0al.: Reading digits in natural images with unsupervised feature learning. In: NIPS workshop on deep learning and unsupervised feature learning. vol.\u00a02011, p.\u00a07. Granada (2011)"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Pang, K., Qi, T., Wu, C., Bai, M., Jiang, M., Huang, Y.: ModelShield: adaptive and robust watermark against model extraction attack. IEEE Transactions on Information Forensics and Security (2025)","DOI":"10.1109\/TIFS.2025.3530691"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: MAP: mask-pruning for source-free model intellectual property protection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23585\u201323594 (2024)","DOI":"10.1109\/CVPR52733.2024.02226"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Peng, Z., Li, S., Chen, G., Zhang, C., Zhu, H., Xue, M.: Fingerprinting deep neural networks globally via universal adversarial perturbations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13430\u201313439 (2022)","DOI":"10.1109\/CVPR52688.2022.01307"},{"key":"15_CR21","unstructured":"Ren, H., et al.: GanFinger: GAN-based fingerprint generation for deep neural network ownership verification. arXiv preprint arXiv:2312.15617 (2023)"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, M., Fu, H., Zhang, D.: Vision-language model IP protection via prompt-based learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 9497\u20139506 (2025)","DOI":"10.1109\/CVPR52734.2025.00887"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, M., Zhang, D., Fu, H.: Model barrier: a compact un-transferable isolation domain for model intellectual property protection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20475\u201320484 (2023)","DOI":"10.1109\/CVPR52729.2023.01961"},{"key":"15_CR26","unstructured":"Wang, L., Xu, S., Xu, R., Wang, X., Zhu, Q.: Non-transferable learning: A new approach for model ownership verification and applicability authorization. arXiv preprint arXiv:2106.06916 (2021)"},{"issue":"5","key":"15_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"G Wilson","year":"2020","unstructured":"Wilson, G., Cook, D.J.: A survey of unsupervised deep domain adaptation. ACM Trans. Intell. Syst. Technol. (TIST) 11(5), 1\u201346 (2020)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Yang, K., Wang, R., Wang, L.: MetaFinger: fingerprinting the deep neural networks with meta-training. In: IJCAI, pp. 776\u2013782 (2022)","DOI":"10.24963\/ijcai.2022\/109"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Zeng, G., Lu, W.: Unsupervised non-transferable text classification. arXiv preprint arXiv:2210.12651 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.685"},{"key":"15_CR30","unstructured":"Zeng, Y., Chen, S., Park, W., Mao, Z.M., Jin, M., Jia, R.: Adversarial unlearning of backdoors via implicit hypergradient. arXiv preprint arXiv:2110.03735 (2021)"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.media.2019.02.010","volume":"54","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Xie, Y., Wu, Q., Xia, Y.: Medical image classification using synergic deep learning. Med. Image Anal. 54, 10\u201319 (2019)","journal-title":"Med. Image Anal."},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, M., Wei, S., Shen, L., Fan, Y., Wu, B.: Enhancing fine-tuning based backdoor defense with sharpness-aware minimization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4466\u20134477 (2023)","DOI":"10.1109\/ICCV51070.2023.00412"}],"container-title":["Lecture Notes in Computer Science","Data Security and Privacy Protection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3185-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T06:06:07Z","timestamp":1760421967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3185-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"ISBN":["9789819531844","9789819531851"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3185-1_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"13 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Security and Privacy Protection","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dspp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dspp2025.xidian.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}