{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:43:58Z","timestamp":1781113438743,"version":"3.54.1"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197710","type":"print"},{"value":"9783031197727","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19772-7_31","type":"book-chapter","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:09:58Z","timestamp":1666908598000},"page":"529-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Shape Matters: Deformable Patch Attack"],"prefix":"10.1007","author":[{"given":"Zhaoyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghe","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouhong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"31_CR1","unstructured":"Athalye, A., Carlini, N., Wagner, D.A.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018. Proceedings of Machine Learning Research, vol. 80, pp. 274\u2013283. PMLR (2018). http:\/\/proceedings.mlr.press\/v80\/athalye18a.html"},{"issue":"4","key":"31_CR2","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1109\/34.993558","volume":"24","author":"SJ Belongie","year":"2002","unstructured":"Belongie, S.J., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509\u2013522 (2002). https:\/\/doi.org\/10.1109\/34.993558","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR3","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch (2017). http:\/\/arxiv.org\/abs\/1712.09665"},{"key":"31_CR4","unstructured":"Chen, C., Zhang, J., Lyu, L.: Gear: a margin-based federated adversarial training approach. In: International Workshop on Trustable, Verifiable, and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI-22) (2022)"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, B., Xu, J., Wu, S., Ding, S., Zhang, W.: Towards practical certifiable patch defense with vision transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15148\u201315158, June 2022","DOI":"10.1109\/CVPR52688.2022.01472"},{"key":"31_CR6","unstructured":"Chiang, P., Ni, R., Abdelkader, A., Zhu, C., Studer, C., Goldstein, T.: Certified defenses for adversarial patches. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020). https:\/\/openreview.net\/forum?id=HyeaSkrYPH"},{"key":"31_CR7","doi-asserted-by":"publisher","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22\u201329 October 2017, pp. 764\u2013773. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.89","DOI":"10.1109\/ICCV.2017.89"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Ding, L., et al.: Towards universal physical attacks on single object tracking. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 1236\u20131245. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16211","DOI":"10.1609\/aaai.v35i2.16211"},{"key":"31_CR9","unstructured":"Dosovitskiy, A., et a.: An image is worth 16x16 words: transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 1625\u20131634. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00175, http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Eykholt_Robust_Physical-World_Attacks_CVPR_2018_paper.html","DOI":"10.1109\/CVPR.2018.00175"},{"key":"31_CR11","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019). https:\/\/openreview.net\/forum?id=Bygh9j09KX"},{"key":"31_CR12","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). http:\/\/arxiv.org\/abs\/1412.6572"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Gu, Z., et al.: Spatiotemporal inconsistency learning for deepfake video detection. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3473\u20133481 (2021)","DOI":"10.1145\/3474085.3475508"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Ma, L.: Delving into the local: dynamic inconsistency learning for deepfake video detection. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i1.19955"},{"key":"31_CR15","doi-asserted-by":"publisher","first-page":"3239","DOI":"10.1109\/TIP.2019.2958144","volume":"29","author":"Z Gu","year":"2020","unstructured":"Gu, Z., Li, F., Fang, F., Zhang, G.: A novel retinex-based fractional-order variational model for images with severely low light. IEEE Trans. Image Process. 29, 3239\u20133253 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"31_CR16","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.apm.2018.11.052","volume":"68","author":"Z Gu","year":"2019","unstructured":"Gu, Z., Li, F., Lv, X.G.: A detail preserving variational model for image retinex. Appl. Math. Model. 68, 643\u2013661 (2019)","journal-title":"Appl. Math. Model."},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Gu, Z., Yao, T., Yang, C., Yi, R., Ding, S., Ma, L.: Region-aware temporal inconsistency learning for deepfake video detection. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (2022)","DOI":"10.24963\/ijcai.2022\/129"},{"key":"31_CR18","doi-asserted-by":"publisher","unstructured":"Hayes, J.: On visible adversarial perturbations & digital watermarking. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 1597\u20131604. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPRW.2018.00210, http:\/\/openaccess.thecvf.com\/content_cvpr_2018_workshops\/w32\/html\/Hayes_On_Visible_Adversarial_CVPR_2018_paper.html","DOI":"10.1109\/CVPRW.2018.00210"},{"key":"31_CR19","doi-asserted-by":"publisher","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 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR20","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 2261\u20132269. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"31_CR21","doi-asserted-by":"publisher","unstructured":"Huang, H., Wang, Y., Chen, Z., Tang, Z., Zhang, W., Ma, K.: Rpattack: refined patch attack on general object detectors. In: 2021 IEEE International Conference on Multimedia and Expo, ICME 2021, Shenzhen, China, 5\u20139 July 2021, pp. 1\u20136. IEEE (2021). https:\/\/doi.org\/10.1109\/ICME51207.2021.9428443","DOI":"10.1109\/ICME51207.2021.9428443"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: CMUA-watermark: a cross-model universal adversarial watermark for combating deepfakes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 989\u2013997 (2022)","DOI":"10.1609\/aaai.v36i1.19982"},{"key":"31_CR23","doi-asserted-by":"publisher","unstructured":"Huang, L., Gao, C., Zhou, Y., Xie, C., Yuille, A.L., Zou, C., Liu, N.: Universal physical camouflage attacks on object detectors. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 717\u2013726. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00080, https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Huang_Universal_Physical_Camouflage_Attacks_on_Object_Detectors_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.00080"},{"key":"31_CR24","unstructured":"Karmon, D., Zoran, D., Goldberg, Y.: Lavan: localized and visible adversarial noise. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018. Proceedings of Machine Learning Research, vol. 80, pp. 2512\u20132520. PMLR (2018). http:\/\/proceedings.mlr.press\/v80\/karmon18a.html"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Kong, X., Liu, X., Gu, J., Qiao, Y., Dong, C.: Reflash dropout in image super-resolution. arXiv preprint arXiv:2112.12089 (2021)","DOI":"10.1109\/CVPR52688.2022.00591"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhao, H., Qiao, Y., Dong, C.: ClassSR: a general framework to accelerate super-resolution networks by data characteristic. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12016\u201312025, June 2021","DOI":"10.1109\/CVPR46437.2021.01184"},{"key":"31_CR27","unstructured":"Levine, A., Feizi, S.: (de)randomized smoothing for certifiable defense against patch attacks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 2020, pp. 6\u201312. Virtual (2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/47ce0875420b2dbacfc5535f94e68433-Abstract.html"},{"key":"31_CR28","doi-asserted-by":"publisher","unstructured":"Li, B., Sun, Z., Guo, Y.: SuperVAE: superpixelwise variational autoencoder for salient object detection. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, 27 January\u20131 February 2019, pp. 8569\u20138576. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33018569","DOI":"10.1609\/aaai.v33i01.33018569"},{"key":"31_CR29","doi-asserted-by":"publisher","unstructured":"Li, B., Sun, Z., Li, Q., Wu, Y., Hu, A.: Group-wise deep object co-segmentation with co-attention recurrent neural network. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October \u20132 November 2019, pp. 8518\u20138527. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00861","DOI":"10.1109\/ICCV.2019.00861"},{"key":"31_CR30","doi-asserted-by":"publisher","unstructured":"Li, B., Sun, Z., Tang, L., Hu, A.: Two-b-real net: two-branch network for real-time salient object detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12\u201317 May 2019, pp. 1662\u20131666. IEEE (2019). https:\/\/doi.org\/10.1109\/ICASSP.2019.8683022","DOI":"10.1109\/ICASSP.2019.8683022"},{"key":"31_CR31","doi-asserted-by":"crossref","unstructured":"Li, B., Sun, Z., Tang, L., Sun, Y., Shi, J.: Detecting robust co-saliency with recurrent co-attention neural network. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 818\u2013825. ijcai.org (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/115","DOI":"10.24963\/ijcai.2019\/115"},{"key":"31_CR32","doi-asserted-by":"publisher","unstructured":"Li, B., Sun, Z., Wang, Q., Li, Q.: Co-saliency detection based on hierarchical consistency. In: Amsaleg, L., et al. (eds.) Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, 21\u201325 October 2019, pp. 1392\u20131400. ACM (2019). https:\/\/doi.org\/10.1145\/3343031.3351016","DOI":"10.1145\/3343031.3351016"},{"key":"31_CR33","doi-asserted-by":"publisher","unstructured":"Li, B., Xu, J., Wu, S., Ding, S., Li, J., Huang, F.: Detecting adversarial patch attacks through global-local consistency. In: Song, D., et al. (eds.) ADVM 2021: Proceedings of the 1st International Workshop on Adversarial Learning for Multimedia, Virtual Event, China, 20 October 2021, pp. 35\u201341. ACM (2021). https:\/\/doi.org\/10.1145\/3475724.3483606","DOI":"10.1145\/3475724.3483606"},{"key":"31_CR34","unstructured":"Li, Y., et al.: Shape-texture debiased neural network training. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=Db4yerZTYkz"},{"key":"31_CR35","doi-asserted-by":"publisher","unstructured":"Liu, A., et al.: Perceptual-sensitive GAN for generating adversarial patches. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January\u20131 February 2019, pp. 1028\u20131035. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33011028","DOI":"10.1609\/aaai.v33i01.33011028"},{"key":"31_CR36","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Efficient universal shuffle attack for visual object tracking. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2739\u20132743. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9747773"},{"key":"31_CR37","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows (2021). https:\/\/arxiv.org\/abs\/2103.14030","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"31_CR38","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, 30 April \u20133 May 2018, Conference Track Proceedings. OpenReview.net (2018). https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"issue":"1","key":"31_CR39","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1011174803800","volume":"43","author":"J Malik","year":"2001","unstructured":"Malik, J., Belongie, S.J., Leung, T.K., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7\u201327 (2001). https:\/\/doi.org\/10.1023\/A:1011174803800","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR40","doi-asserted-by":"publisher","unstructured":"Naseer, M., Khan, S., Porikli, F.: Local gradients smoothing: defense against localized adversarial attacks. In: IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, HI, USA, 7\u201311 January 2019, pp. 1300\u20131307. IEEE (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00143","DOI":"10.1109\/WACV.2019.00143"},{"key":"31_CR41","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, pp. 8\u201314, December 2019. Vancouver, BC, Canada, pp. 8024\u20138035 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html"},{"key":"31_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/978-3-030-68238-5_32","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"S Rao","year":"2020","unstructured":"Rao, S., Stutz, D., Schiele, B.: Adversarial training against location-optimized adversarial patches. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12539, pp. 429\u2013448. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-68238-5_32"},{"key":"31_CR43","doi-asserted-by":"publisher","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"31_CR44","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetv 2: inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 4510\u20134520. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474, http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.html","DOI":"10.1109\/CVPR.2018.00474"},{"key":"31_CR45","doi-asserted-by":"publisher","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Weippl, E.R., Katzenbeisser, S., Kruegel, C., Myers, A.C., Halevi, S. (eds.) Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 24\u201328 October 2016, pp. 1528\u20131540. ACM (2016). https:\/\/doi.org\/10.1145\/2976749.2978392","DOI":"10.1145\/2976749.2978392"},{"key":"31_CR46","unstructured":"Shen, T., et al.: Federated mutual learning. arXiv preprint arXiv:2006.16765 (2020)"},{"key":"31_CR47","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. 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). http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"31_CR48","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14\u201316 April 2014, Conference Track Proceedings (2014). http:\/\/arxiv.org\/abs\/1312.6199"},{"key":"31_CR49","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp. 6105\u20136114. PMLR (2019). http:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"31_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/978-3-030-69535-4_26","volume-title":"Computer Vision \u2013 ACCV 2020","author":"L Tang","year":"2021","unstructured":"Tang, L., Li, B.: CLASS: cross-level attention and supervision for salient objects detection. In: Ishikawa, H., Liu, C.-L., Pajdla, T., Shi, J. (eds.) ACCV 2020. LNCS, vol. 12624, pp. 420\u2013436. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69535-4_26"},{"key":"31_CR51","doi-asserted-by":"publisher","unstructured":"Tang, L., Li, B., Zhong, Y., Ding, S., Song, M.: Disentangled high quality salient object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10\u201317 October 2021, pp. 3560\u20133570. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00356","DOI":"10.1109\/ICCV48922.2021.00356"},{"key":"31_CR52","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.inffus.2022.03.009","volume":"83\u201384","author":"Y Wang","year":"2022","unstructured":"Wang, Y., et al.: A systematic review on affective computing: emotion models, databases, and recent advances. Inf. Fusion 83\u201384, 19\u201352 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2022.03.009","journal-title":"Inf. Fusion"},{"key":"31_CR53","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Ferv39k: a large-scale multi-scene dataset for facial expression recognition in videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20922\u201320931, June 2022","DOI":"10.1109\/CVPR52688.2022.02025"},{"key":"31_CR54","unstructured":"Wu, T., Tong, L., Vorobeychik, Y.: Defending against physically realizable attacks on image classification. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020, OpenReview.net (2020). https:\/\/openreview.net\/forum?id=H1xscnEKDr"},{"key":"31_CR55","unstructured":"Xiang, C., Bhagoji, A.N., Sehwag, V., Mittal, P.: PatchGuard: a provably robust defense against adversarial patches via small receptive fields and masking. In: Bailey, M., Greenstadt, R. (eds.) 30th USENIX Security Symposium, USENIX Security 2021, 11\u201313 August 2021, pp. 2237\u20132254. USENIX Association (2021). https:\/\/www.usenix.org\/conference\/usenixsecurity21\/presentation\/xiang"},{"key":"31_CR56","doi-asserted-by":"publisher","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.L.: Adversarial examples for semantic segmentation and object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22\u201329 October 2017, pp. 1378\u20131387. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.153","DOI":"10.1109\/ICCV.2017.153"},{"key":"31_CR57","doi-asserted-by":"publisher","unstructured":"Xie, E., et al.: Polarmask: single shot instance segmentation with polar representation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 12190\u201312199. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01221","DOI":"10.1109\/CVPR42600.2020.01221"},{"key":"31_CR58","unstructured":"Zhang, J., Chen, C., Dong, J., Jia, R., Lyu, L.: QEKD: query-efficient and data-free knowledge distillation from black-box models. arXiv preprint arXiv:2205.11158 (2022)"},{"key":"31_CR59","unstructured":"Zhang, J., et al.: A practical data-free approach to one-shot federated learning with heterogeneity. arXiv preprint arXiv:2112.12371 (2021)"},{"key":"31_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Towards efficient data free black-box adversarial attack. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15115\u201315125, June 2022","DOI":"10.1109\/CVPR52688.2022.01469"},{"key":"31_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, L., Li, G., Wu, C.: Adversarial examples for good: adversarial examples guided imbalanced learning. arXiv preprint arXiv:2201.12356 (2022)","DOI":"10.1109\/ICIP46576.2022.9897634"},{"key":"31_CR62","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Yuan, B., McCoyd, M., Wagner, D.A.: Clipped bagnet: defending against sticker attacks with clipped bag-of-features. In: 2020 IEEE Security and Privacy Workshops, SP Workshops, San Francisco, CA, USA, 21 May 2020, pp. 55\u201361. IEEE (2020). https:\/\/doi.org\/10.1109\/SPW50608.2020.00026","DOI":"10.1109\/SPW50608.2020.00026"},{"key":"31_CR63","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-67070-2_3","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"H Zhao","year":"2020","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Yu., Dong, C.: Efficient image super-resolution using pixel attention. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 56\u201372. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-67070-2_3"},{"key":"31_CR64","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., Ding, S.: Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4504\u20134513, June 2022","DOI":"10.1109\/CVPR52688.2022.00446"},{"key":"31_CR65","unstructured":"Zhong, Y., Li, B., Tang, L., Tang, H., Ding, S.: Highly efficient natural image matting. CoRR abs\/2110.12748 (2021), https:\/\/arxiv.org\/abs\/2110.12748"},{"key":"31_CR66","doi-asserted-by":"crossref","unstructured":"Zhou, Q., et al.: Uncertainty-aware consistency regularization for cross-domain semantic segmentation. In: Computer Vision and Image Understanding, p. 103448 (2022)","DOI":"10.1016\/j.cviu.2022.103448"},{"key":"31_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhang, K.Y., Yao, T., Yi, R., Ding, S., Ma, L.: Adaptive mixture of experts learning for generalizable face anti-spoofing. In: Proceedings of the 30th ACM International Conference on Multimedia (2022)","DOI":"10.1145\/3503161.3547769"},{"key":"31_CR68","doi-asserted-by":"publisher","unstructured":"Zhou, Q., et al.: Generative domain adaptation for face anti-spoofing. In: Avidan, S., et al. (eds.) ECCV 2022. LNCS, vol. 13665, pp. 335\u2013356. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20065-6_20","DOI":"10.1007\/978-3-031-20065-6_20"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19772-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:54:57Z","timestamp":1710262497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19772-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197710","9783031197727"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19772-7_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}