{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T11:10:30Z","timestamp":1778238630452,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570836","type":"print"},{"value":"9789819570843","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-7084-3_1","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T10:24:26Z","timestamp":1778235866000},"page":"3-19","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TVL-Filter: Total Variation Loss\u2013Based Sample Filter for\u00a0Efficient Adversarial Detection"],"prefix":"10.1007","author":[{"given":"Fei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuran","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: a query-efficient black-box adversarial attack via random search. In: Computer Vision \u2013 ECCV 2020 (2020)","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Basu, S., Gupta, M., Madan, C., Gupta, P., Arora, C.: FocusMAE: gallbladder cancer detection from ultrasound videos with focused masked autoencoders. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11715\u201311725 (2024)","DOI":"10.1109\/CVPR52733.2024.01113"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Cassavia, N., Folino, F., Guarascio, M.: Detecting dos and DDoS attacks through sparse U-Net-like autoencoders. In: 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1342\u20131346 (2022)","DOI":"10.1109\/ICTAI56018.2022.00203"},{"key":"1_CR4","unstructured":"Dziugaite, G.K., Ghahramani, Z., Roy, D.M.: A study of the effect of JPG compression on adversarial images. Int. Soc. Bayesian Anal. (ISBA) (2016)"},{"key":"1_CR5","unstructured":"Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., Madry, A.: Exploring the landscape of spatial robustness. In: International Conference on Machine Learning (2017)"},{"key":"1_CR6","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"1_CR7","first-page":"1","volume":"55","author":"S Han","year":"2023","unstructured":"Han, S., Lin, C., Shen, C., Wang, Q., Guan, X.: Interpreting adversarial examples in deep learning: A review. ACM Comput. Surv. 55, 1\u201338 (2023)","journal-title":"ACM Comput. Surv."},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR9","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2012)","journal-title":"Commun. ACM"},{"key":"1_CR11","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Workshop Track Proceedings. OpenReview.net (2017)"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Ma, S., Liu, Y., Tao, G., Lee, W.C., Zhang, X.: NIC: detecting adversarial samples with neural network invariant checking. In: Proceedings 2019 Network and Distributed System Security Symposium (2019)","DOI":"10.14722\/ndss.2019.23415"},{"key":"1_CR14","unstructured":"Ma, X., et al.: Characterizing adversarial subspaces using local intrinsic dimensionality. In: International Conference on Learning Representations (2018)"},{"key":"1_CR15","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574\u20132582 (2015)","DOI":"10.1109\/CVPR.2016.282"},{"issue":"5","key":"1_CR17","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1109\/TITS.2020.2976572","volume":"22","author":"A Ndikumana","year":"2021","unstructured":"Ndikumana, A., Tran, N.H., Kim, D.H., Kim, K.T., Hong, C.S.: Deep learning based caching for self-driving cars in multi-access edge computing. IEEE Trans. Intell. Transp. Syst. 22(5), 2862\u20132877 (2021). https:\/\/doi.org\/10.1109\/TITS.2020.2976572","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR18","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"1_CR19","unstructured":"Nicolae, M.I., et al.: Adversarial robustness toolbox v1.0.0 (2018). arXiv: Learning"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Ramanathan, T., Manimaran, A., You, S., Kuo, C.C.J.: Robustness of Saak transform against adversarial attacks. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2531\u20132535 (2019)","DOI":"10.1109\/ICIP.2019.8803240"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Rouhani, B.D., Samragh, M., Javaheripi, M., Javidi, T., Koushanfar, F.: DeepFense: online accelerated defense against adversarial deep learning. In: 2018 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), pp.\u00a01\u20138 (2017)","DOI":"10.1145\/3240765.3240791"},{"key":"1_CR22","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259\u2013268 (1992)","journal-title":"Physica D"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Samavatian, M.H., Majumdar, S., Barber, K., Teodorescu, R.: HASI: hardware-accelerated stochastic inference, a defense against adversarial machine learning attacks. ArXiv (2021)","DOI":"10.1109\/HOST49136.2021.9702287"},{"key":"1_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, X., Yin, P., Xing, E.P.: High-frequency component helps explain the generalization of convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8681\u20138691 (2019)","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: DNNGuard: an elastic heterogeneous DNN accelerator architecture against adversarial attacks. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (2020)","DOI":"10.1145\/3373376.3378532"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Wei, Y., et al.: Editable scene simulation for autonomous driving via collaborative LLM-Agents. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15077\u201315087 (2024)","DOI":"10.1109\/CVPR52733.2024.01428"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Xu, W., Evans, D., Qi, Y.: Feature squeezing: detecting adversarial examples in deep neural networks. In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, 18\u201321 February 2018. The Internet Society (2018)","DOI":"10.14722\/ndss.2018.23198"},{"key":"1_CR29","unstructured":"Yao, L., Miller, J.A., Stanford: tiny ImageNet classification with convolutional neural networks (2015). https:\/\/api.semanticscholar.org\/CorpusID:196590285"},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"102788","DOI":"10.1016\/j.artmed.2024.102788","volume":"149","author":"D Zhang","year":"2024","unstructured":"Zhang, D., Wang, C., Chen, T., Chen, W., Shen, Y.: Scalable Swin transformer network for brain tumor segmentation from incomplete MRI modalities. Artif. Intell. Med. 149, 102788 (2024)","journal-title":"Artif. Intell. Med."},{"key":"1_CR31","doi-asserted-by":"publisher","first-page":"103524","DOI":"10.1016\/j.cose.2023.103524","volume":"136","author":"X Zhao","year":"2023","unstructured":"Zhao, X., Jiang, R., Han, Y., Li, A., Peng, Z.: A survey on cybersecurity knowledge graph construction. Comput. Secur. 136, 103524 (2023)","journal-title":"Comput. Secur."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7084-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T10:24:48Z","timestamp":1778235888000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7084-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570836","9789819570843"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7084-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","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":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}