{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:30:40Z","timestamp":1743107440750,"version":"3.40.3"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031728471"},{"type":"electronic","value":"9783031728488"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72848-8_16","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:36:03Z","timestamp":1732800963000},"page":"269-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Out-of-Bounding-Box Triggers: A Stealthy Approach to\u00a0Cheat Object Detectors"],"prefix":"10.1007","author":[{"given":"Tao","family":"Lin","sequence":"first","affiliation":[]},{"given":"Lijia","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Gaojie","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Renjue","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Aich, A., Li, S., Song, C., Asif, M.S., Krishnamurthy, S.V., Roy-Chowdhury, A.K.: Leveraging local patch differences in multi-object scenes for generative adversarial attacks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1308\u20131318 (2023)","DOI":"10.1109\/WACV56688.2023.00136"},{"key":"16_CR2","unstructured":"Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: Synthesizing robust adversarial examples. In: International Conference on Machine Learning, pp. 284\u2013293. PMLR (2018)"},{"key":"16_CR3","unstructured":"Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: reliable attacks against black-box machine learning models. arXiv preprint arXiv:1712.04248 (2017)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Cao, Y., et al.: Adversarial sensor attack on LiDAR-based perception in autonomous driving. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2267\u20132281 (2019)","DOI":"10.1145\/3319535.3339815"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C., Seff, A., Kornhauser, A., Xiao, J.: DeepDriving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2722\u20132730 (2015)","DOI":"10.1109\/ICCV.2015.312"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Jordan, M.I., Wainwright, M.J.: HopSkipJumpAttack: a query-efficient decision-based attack. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 1277\u20131294. IEEE (2020)","DOI":"10.1109\/SP40000.2020.00045"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 15\u201326 (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"16_CR9","unstructured":"Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: International Conference on Machine Learning, pp. 2206\u20132216. PMLR (2020)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641 (2019)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4312\u20134321 (2019)","DOI":"10.1109\/CVPR.2019.00444"},{"key":"16_CR14","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Conference on Robot Learning, pp. 1\u201316. PMLR (2017)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Duan, R., Ma, X., Wang, Y., Bailey, J., Qin, A.K., Yang, Y.: Adversarial camouflage: hiding physical-world attacks with natural styles. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1000\u20131008 (2020)","DOI":"10.1109\/CVPR42600.2020.00108"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1625\u20131634 (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"16_CR17","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"issue":"3","key":"16_CR18","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1002\/rob.21918","volume":"37","author":"S Grigorescu","year":"2020","unstructured":"Grigorescu, S., Trasnea, B., Cocias, T., Macesanu, G.: A survey of deep learning techniques for autonomous driving. J. Field Robot. 37(3), 362\u2013386 (2020)","journal-title":"J. Field Robot."},{"key":"16_CR19","unstructured":"Hoory, S., Shapira, T., Shabtai, A., Elovici, Y.: Dynamic adversarial patch for evading object detection models. arXiv preprint arXiv:2010.13070 (2020)"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Hu, Y.C.T., Kung, B.H., Tan, D.S., Chen, J.C., Hua, K.L., Cheng, W.H.: Naturalistic physical adversarial patch for object detectors. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7848\u20137857 (2021)","DOI":"10.1109\/ICCV48922.2021.00775"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Z., Huang, S., Zhu, X., Sun, F., Zhang, B., Hu, X.: Adversarial texture for fooling person detectors in the physical world. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13307\u201313316 (2022)","DOI":"10.1109\/CVPR52688.2022.01295"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Huang, L., et al.: Universal physical camouflage attacks on object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 720\u2013729 (2020)","DOI":"10.1109\/CVPR42600.2020.00080"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhao, X., Jin, G., Huang, X.: SAFARI: versatile and efficient evaluations for robustness of interpretability. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1988\u20131998 (2023)","DOI":"10.1109\/ICCV51070.2023.00190"},{"key":"16_CR24","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., Lin, J.: Black-box adversarial attacks with limited queries and information. In: International Conference on Machine Learning, pp. 2137\u20132146. PMLR (2018)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Im\u00a0Choi, J., Tian, Q.: Adversarial attack and defense of YOLO detectors in autonomous driving scenarios. In: 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 1011\u20131017. IEEE (2022)","DOI":"10.1109\/IV51971.2022.9827222"},{"key":"16_CR26","unstructured":"Jia, S., et al.: Adv-Attribute: inconspicuous and transferable adversarial attack on face recognition. In: Advances in Neural Information Processing Systems 35, pp. 34136\u201334147 (2022)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Jin, G., Yi, X., Huang, W., Schewe, S., Huang, X.: Enhancing adversarial training with second-order statistics of weights. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15273\u201315283 (2022)","DOI":"10.1109\/CVPR52688.2022.01484"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Jin, G., Yi, X., Wu, D., Mu, R., Huang, X.: Randomized adversarial training via Taylor expansion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16447\u201316457 (2023)","DOI":"10.1109\/CVPR52729.2023.01578"},{"key":"16_CR29","unstructured":"Jocher, G., et\u00a0al.: ultralytics\/yolov5: v7. 0-YOLOv5 SOTA realtime instance segmentation. Zenodo (2022)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Kahla, M., Chen, S., Just, H.A., Jia, R.: Label-only model inversion attacks via boundary repulsion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15045\u201315053 (2022)","DOI":"10.1109\/CVPR52688.2022.01462"},{"key":"16_CR31","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S., et\u00a0al.: Adversarial examples in the physical world (2016)"},{"key":"16_CR32","unstructured":"Lee, M., Kolter, Z.: On physical adversarial patches for object detection. arXiv preprint arXiv:1906.11897 (2019)"},{"key":"16_CR33","doi-asserted-by":"publisher","first-page":"27217","DOI":"10.1109\/ACCESS.2022.3215762","volume":"11","author":"X Lei","year":"2022","unstructured":"Lei, X., Cai, X., Lu, C., Jiang, Z., Gong, Z., Lu, L.: Using frequency attention to make adversarial patch powerful against person detector. IEEE Access 11, 27217\u201327225 (2022)","journal-title":"IEEE Access"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4282\u20134291 (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971\u20138980 (2018)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"16_CR36","unstructured":"Li, J., Schmidt, F., Kolter, Z.: Adversarial camera stickers: a physical camera-based attack on deep learning systems. In: International Conference on Machine Learning, pp. 3896\u20133904. PMLR (2019)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Y., Dai, X., Guo, S., Xiao, B.: Physical-world optical adversarial attacks on 3D face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24699\u201324708 (2023)","DOI":"10.1109\/CVPR52729.2023.02366"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Liang, S., Wu, B., Fan, Y., Wei, X., Cao, X.: Parallel rectangle flip attack: a query-based black-box attack against object detection. arXiv preprint arXiv:2201.08970 (2022)","DOI":"10.1109\/ICCV48922.2021.00760"},{"key":"16_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Liu, A., et al.: Perceptual-sensitive GAN for generating adversarial patches. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 1028\u20131035 (2019)","DOI":"10.1609\/aaai.v33i01.33011028"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Liu, H., Wu, Y., Yu, Z., Vorobeychik, Y., Zhang, N.: SlowLiDAR: increasing the latency of LiDAR-based detection using adversarial examples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5146\u20135155 (2023)","DOI":"10.1109\/CVPR52729.2023.00498"},{"key":"16_CR42","unstructured":"Liu, X., Yang, H., Liu, Z., Song, L., Li, H., Chen, Y.: DPatch: an adversarial patch attack on object detectors. arXiv preprint arXiv:1806.02299 (2018)"},{"key":"16_CR43","unstructured":"Lovisotto, G., Turner, H., Sluganovic, I., Strohmeier, M., Martinovic, I.: SLAP: improving physical adversarial examples with Short-Lived adversarial perturbations. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 1865\u20131882 (2021)"},{"key":"16_CR44","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5188\u20135196 (2015)","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Pomponi, J., Scardapane, S., Uncini, A.: Pixle: a fast and effective black-box attack based on rearranging pixels. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892966"},{"key":"16_CR48","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Shapira, A., Zolfi, A., Demetrio, L., Biggio, B., Shabtai, A.: Phantom sponges: exploiting non-maximum suppression to attack deep object detectors. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4571\u20134580 (2023)","DOI":"10.1109\/WACV56688.2023.00455"},{"key":"16_CR51","doi-asserted-by":"crossref","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: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1528\u20131540 (2016)","DOI":"10.1145\/2976749.2978392"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Shi, Z., Yang, W., Xu, Z., Yu, Z., Huang, L.: Reinforcement learning-based adversarial attacks on object detectors using reward shaping. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 8424\u20138432 (2023)","DOI":"10.1145\/3581783.3612304"},{"key":"16_CR53","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Thys, S., Van\u00a0Ranst, W., Goedem\u00e9, T.: Fooling automated surveillance cameras: adversarial patches to attack person detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00012"},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Tu, J., et al.: Physically realizable adversarial examples for LiDAR object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13716\u201313725 (2020)","DOI":"10.1109\/CVPR42600.2020.01373"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: FCA: learning a 3D full-coverage vehicle camouflage for multi-view physical adversarial attack. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2414\u20132422 (2022)","DOI":"10.1609\/aaai.v36i2.20141"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Wang, D., Yao, W., Jiang, T., Li, C., Chen, X.: RFLA: a stealthy reflected light adversarial attack in the physical world. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4455\u20134465 (2023)","DOI":"10.1109\/ICCV51070.2023.00411"},{"key":"16_CR58","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":"16_CR59","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, A., Yin, Z., Liu, S., Tang, S., Liu, X.: Dual attention suppression attack: generate adversarial camouflage in physical world. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8565\u20138574 (2021)","DOI":"10.1109\/CVPR46437.2021.00846"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1328\u20131338 (2019)","DOI":"10.1109\/CVPR.2019.00142"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Wang, X., He, K.: Enhancing the transferability of adversarial attacks through variance tuning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1924\u20131933 (2021)","DOI":"10.1109\/CVPR46437.2021.00196"},{"key":"16_CR62","unstructured":"Wu, D., Wang, Y., Xia, S.T., Bailey, J., Ma, X.: Skip connections matter: on the transferability of adversarial examples generated with ResNets. arXiv preprint arXiv:2002.05990 (2020)"},{"key":"16_CR63","doi-asserted-by":"crossref","unstructured":"Wu, W., et al.: Boosting the transferability of adversarial samples via attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1161\u20131170 (2020)","DOI":"10.1109\/CVPR42600.2020.00124"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2730\u20132739 (2019)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"16_CR65","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1007\/978-3-030-58558-7_39","volume-title":"Computer Vision \u2013 ECCV 2020","author":"K Xu","year":"2020","unstructured":"Xu, K., et al.: Adversarial T-Shirt! Evading person detectors in a physical world. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 665\u2013681. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_39"},{"key":"16_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Improving adversarial transferability via neuron attribution-based attacks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14993\u201315002 (2022)","DOI":"10.1109\/CVPR52688.2022.01457"},{"key":"16_CR67","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, pp. 15115\u201315125 (2022)","DOI":"10.1109\/CVPR52688.2022.01469"},{"key":"16_CR68","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xu, N., Yang, P., Jin, G., Huang, C.C., Zhang, L.: TrajPAC: towards robustness verification of pedestrian trajectory prediction models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8327\u20138339 (2023)","DOI":"10.1109\/ICCV51070.2023.00765"},{"key":"16_CR69","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhu, H., Liang, R., Shen, Q., Zhang, S., Chen, K.: Seeing isn\u2019t believing: towards more robust adversarial attack against real world object detectors. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1989\u20132004 (2019)","DOI":"10.1145\/3319535.3354259"},{"key":"16_CR70","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Liu, X., Zhai, D., Jiang, J., Ji, X.: Shadows can be dangerous: stealthy and effective physical-world adversarial attack by natural phenomenon. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15345\u201315354 (2022)","DOI":"10.1109\/CVPR52688.2022.01491"},{"issue":"3","key":"16_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3559758","volume":"41","author":"L Zhu","year":"2023","unstructured":"Zhu, L., Wang, T., Li, J., Zhang, Z., Shen, J., Wang, X.: Efficient query-based black-box attack against cross-modal hashing retrieval. ACM Trans. Inf. Syst. 41(3), 1\u201325 (2023)","journal-title":"ACM Trans. Inf. Syst."},{"key":"16_CR72","doi-asserted-by":"crossref","unstructured":"Zhu, X., Li, X., Li, J., Wang, Z., Hu, X.: Fooling thermal infrared pedestrian detectors in real world using small bulbs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 3616\u20133624 (2021)","DOI":"10.1609\/aaai.v35i4.16477"},{"key":"16_CR73","doi-asserted-by":"crossref","unstructured":"Zolfi, A., Kravchik, M., Elovici, Y., Shabtai, A.: The translucent patch: a physical and universal attack on object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15232\u201315241 (2021)","DOI":"10.1109\/CVPR46437.2021.01498"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72848-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T14:09:59Z","timestamp":1732802999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72848-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031728471","9783031728488"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72848-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}