{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:27:12Z","timestamp":1745987232844,"version":"3.40.4"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"The Natural Science Foundation of Fujian Province","award":["2024J08276"],"award-info":[{"award-number":["2024J08276"]}]},{"name":"The Key Research Project for Young and Middle-aged Researchers by the Fujian Provincial Department of Education","award":["JZ230044"],"award-info":[{"award-number":["JZ230044"]}]},{"name":"The Open Research Project of Fujian Province Key Laboratory of Financial Information Processing","award":["JXC202402"],"award-info":[{"award-number":["JXC202402"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09551-z","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T02:32:20Z","timestamp":1745980340000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards auditing gradient privacy risks in image reconstruction attacks on deep learning models"],"prefix":"10.1007","volume":"28","author":[{"given":"Tao","family":"Huang","sequence":"first","affiliation":[]},{"given":"Xin","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Qingyu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ziyang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Chenhuang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Guolong","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wencheng","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"issue":"1","key":"9551_CR1","first-page":"7068349","volume":"2018","author":"A Voulodimos","year":"2018","unstructured":"Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience. 2018;2018(1):7068349.","journal-title":"Computational intelligence and neuroscience"},{"key":"9551_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100134","volume":"6","author":"J Chai","year":"2021","unstructured":"Chai J, Zeng H, Li A, Ngai EW. Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications. 2021;6: 100134.","journal-title":"Machine Learning with Applications"},{"issue":"2","key":"9551_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1177\/0025802419893168","volume":"60","author":"P Kaur","year":"2020","unstructured":"Kaur P, Krishan K, Sharma SK, Kanchan T. Facial-recognition algorithms: A literature review. Medicine, Science and the Law. 2020;60(2):131\u20139.","journal-title":"Medicine, Science and the Law"},{"key":"9551_CR4","doi-asserted-by":"publisher","first-page":"139110","DOI":"10.1109\/ACCESS.2020.3011028","volume":"8","author":"L Li","year":"2020","unstructured":"Li L, Mu X, Li S, Peng H. A review of face recognition technology. IEEE access. 2020;8:139110\u201320.","journal-title":"IEEE access"},{"issue":"1","key":"9551_CR5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","volume":"4","author":"R Aggarwal","year":"2021","unstructured":"Aggarwal R, Sounderajah V, Martin G, Ting DS, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ digital medicine. 2021;4(1):65.","journal-title":"NPJ digital medicine"},{"key":"9551_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105458","volume":"145","author":"M Shehab","year":"2022","unstructured":"Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Gandomi AH. Machine learning in medical applications: a review of state-of-the-art methods. Computers in Biology and Medicine. 2022;145: 105458.","journal-title":"Computers in Biology and Medicine"},{"key":"9551_CR7","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1631\/FITEE.1601650","volume":"18","author":"T Zhang","year":"2017","unstructured":"Zhang T, Li Q, Zhang C-S, Liang H-W, Li P, Wang T-M, Li S, Zhu Y-L, Wu C. Current trends in the development of intelligent unmanned autonomous systems. Frontiers of information technology & electronic engineering. 2017;18:68\u201385.","journal-title":"Frontiers of information technology & electronic engineering"},{"issue":"2","key":"9551_CR8","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TITS.2019.2962338","volume":"22","author":"S Kuutti","year":"2020","unstructured":"Kuutti S, Bowden R, Jin Y, Barber P, Fallah S. A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems. 2020;22(2):712\u201333.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"9551_CR9","doi-asserted-by":"crossref","unstructured":"Hesamifard E, Takabi H, Ghasemi M, Wright R.N. Privacy-preserving machine learning as a service. Proceedings on Privacy Enhancing Technologies (2018)","DOI":"10.1515\/popets-2018-0024"},{"key":"9551_CR10","first-page":"994","volume":"34","author":"X Jin","year":"2021","unstructured":"Jin X, Chen P-Y, Hsu C-Y, Yu C-M, Chen T. Cafe: Catastrophic data leakage in vertical federated learning. Advances in Neural Information Processing Systems. 2021;34:994\u20131006.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"9551_CR11","unstructured":"Salem A, Bhattacharya A, Backes M, Fritz M, Zhang Y. $$\\{$$Updates-Leak$$\\}$$: Data set inference and reconstruction attacks in online learning. In: 29th USENIX Security Symposium (USENIX Security 20), 2020:1291\u20131308"},{"key":"9551_CR12","doi-asserted-by":"crossref","unstructured":"Balle B, Cherubin G, Hayes J. Reconstructing training data with informed adversaries. In: 2022 IEEE Symposium on Security and Privacy (SP), 2022:1138\u20131156 . IEEE","DOI":"10.1109\/SP46214.2022.9833677"},{"key":"9551_CR13","doi-asserted-by":"crossref","unstructured":"Hayes J, Melis L, Danezis G, De\u00a0Cristofaro E. Logan: Membership inference attacks against generative models. arXiv preprint arXiv:1705.07663 (2017)","DOI":"10.2478\/popets-2019-0008"},{"key":"9551_CR14","doi-asserted-by":"crossref","unstructured":"Ha T, Dang T.K, Dang T.T, Truong T.A, Nguyen M.T. Differential privacy in deep learning: an overview. In: 2019 International Conference on Advanced Computing and Applications (ACOMP), 2019:97\u2013102 . IEEE","DOI":"10.1109\/ACOMP.2019.00022"},{"key":"9551_CR15","doi-asserted-by":"crossref","unstructured":"Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the gan: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017:603\u2013618","DOI":"10.1145\/3133956.3134012"},{"key":"9551_CR16","doi-asserted-by":"crossref","unstructured":"Papernot ., McDaniel P, Wu X, Jha S, Swami A. Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), 2016:582\u2013597 . IEEE","DOI":"10.1109\/SP.2016.41"},{"key":"9551_CR17","doi-asserted-by":"crossref","unstructured":"Dwork C, Roth A, et al. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 2014;9(3\u20134):211\u2013407","DOI":"10.1561\/0400000042"},{"key":"9551_CR18","doi-asserted-by":"crossref","unstructured":"Ketkar N, Moolayil J, Ketkar N, Moolayil J. Convolutional neural networks. Deep learning with Python: learn best practices of deep learning models with PyTorch, 2021:197\u2013242","DOI":"10.1007\/978-1-4842-5364-9_6"},{"issue":"3","key":"9551_CR19","first-page":"304","volume":"6","author":"R Al-Jawfi","year":"2009","unstructured":"Al-Jawfi R. Handwriting arabic character recognition lenet using neural network. Int Arab J Inf Technol. 2009;6(3):304\u20139.","journal-title":"Int. Arab J. Inf. Technol."},{"key":"9551_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770\u2013778","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09551-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09551-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09551-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T02:32:29Z","timestamp":1745980349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09551-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9551"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09551-z","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,30]]},"assertion":[{"value":"10 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We confirmed that the experiments follow the criteria of ethics approval and consent to participate. No human subjects were harmed in this research. The datasets generated and\/or analyzed during the current study are available from the corresponding author upon reasonable request.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial registration"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Permission to reproduce material from other sources"}},{"value":"The authors declare no competing interests.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"53"}}