{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:47:52Z","timestamp":1770814072487,"version":"3.50.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032163417","type":"print"},{"value":"9783032163424","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\/978-3-032-16342-4_13","type":"book-chapter","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T08:57:53Z","timestamp":1770800273000},"page":"227-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gradient-Guided Adversarial Patch Attack for\u00a0Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Rishav","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Umesh","family":"Kashyap","sequence":"additional","affiliation":[]},{"given":"Sk. Subidh","family":"Ali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Adhikari, A., et al.: Adversarial patch camouflage against aerial detection. In: Proceedings of SPIE\u2014Artificial Intelligence and Machine Learning in Defense Applications II, vol. 11543, p. 115430F. SPIE (2020). https:\/\/doi.org\/10.1117\/12.2575907","DOI":"10.1117\/12.2575907"},{"key":"13_CR2","unstructured":"Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: Synthesizing robust adversarial examples. In: Proceedings of the 35th International Conference on Machine Learning (ICML) (2018)"},{"key":"13_CR3","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, B., Wu, S., Xu, J., Ding, S., Zhang, W.: Shape matters: deformable patch attack. In: European Conference on Computer Vision, pp. 529\u2013548. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19772-7_31"},{"key":"13_CR5","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":"13_CR6","doi-asserted-by":"crossref","unstructured":"Duan, Y., Chen, J., et\u00a0al.: Learning coated adversarial camouflages for object detectors. In: IJCAI (2022)","DOI":"10.24963\/ijcai.2022\/125"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1625\u20131634 (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"13_CR8","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"He, C., et al.: Dorpatch: distributed and occlusion-robust adversarial patch to evade certifiable defenses. In: NDSS (2024)","DOI":"10.14722\/ndss.2024.24920"},{"key":"13_CR10","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Hingun, N., Sitawarin, C., Li, J., Wagner, D.: Reap: a large-scale realistic adversarial patch benchmark. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4640\u20134651 (2023)","DOI":"10.1109\/ICCV51070.2023.00428"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Hu, Y.C.T., Chen, J.C., Kung, B.H., Hua, K.L., Tan, D.S.: Naturalistic physical adversarial patch for object detectors. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00775"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"133049","DOI":"10.1109\/ACCESS.2021.3115764","volume":"9","author":"H Kang","year":"2021","unstructured":"Kang, H., Kim, H., et al.: Robust adversarial attack against explainable deep classification models based on adversarial images with different patch sizes and perturbation ratios. IEEE Access 9, 133049\u2013133061 (2021)","journal-title":"IEEE Access"},{"key":"13_CR15","unstructured":"K\u00fcgler, D., et\u00a0al.: Physical attacks in dermoscopy: an evaluation of robustness for clinical deep-learning (2021)"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Kumar, V., Agarwal, A.: A unified, resilient, and explainable adversarial patch detector. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 30387\u201330397 (2025)","DOI":"10.1109\/CVPR52734.2025.02829"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: Artificial Intelligence Safety and Security, pp. 99\u2013112. Chapman and Hall\/CRC (2018)","DOI":"10.1201\/9781351251389-8"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Lee, H.J., Kim, J.S., Lee, H.J., Choi, S.H.: Poses: patch optimization strategies for efficiency and stealthiness using explainable AI. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3555044"},{"key":"13_CR19","unstructured":"Li, C., Liu, Z., et\u00a0al.: Capgen: an environment-adaptive generator of adversarial patches (2024)"},{"key":"13_CR20","unstructured":"Liu, X., Yang, X., Chen, C., Song, D.: Universal adversarial patch attack against object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013661 (2019)"},{"key":"13_CR21","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":"13_CR22","unstructured":"Miao, B., et al.: Advlogo: adversarial patch attack against object detectors based on diffusion models. arXiv preprint arXiv:2409.07002 (2024)"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Nesti, F., Rossolini, G., Nair, S., Biondi, A., Buttazzo, G.: Evaluating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2280\u20132289 (2022)","DOI":"10.1109\/WACV51458.2022.00288"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Rafferty, A., Ramaesh, R., Rajan, A.: Corpa: adversarial image generation for chest x-rays using concept vector perturbations and generative models. arXiv preprint arXiv:2502.05214 (2025)","DOI":"10.1109\/ICHI64645.2025.00057"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"13_CR26","unstructured":"Shekhar, P., Devkota, B., Samaraweera, D., Kandel, L.N., Babu, M.: Cross-model transferability of adversarial patches in real-time segmentation for autonomous driving. arXiv preprint arXiv:2502.16012 (2025)"},{"key":"13_CR27","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014), published in ICLR 2015"},{"key":"13_CR28","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"13_CR29","unstructured":"Wang, C., Duan, J., Xiao, C., et\u00a0al.: Semantic adversarial attacks via diffusion models. In: BMVC (2023)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, F., He, L.: A unified framework for adversarial patch attacks against visual 3D object detection in autonomous driving. IEEE Trans. Circuits Syst. Video Technol. (2025)","DOI":"10.1109\/TCSVT.2025.3525725"},{"key":"13_CR31","first-page":"67269","volume":"36","author":"P Williams","year":"2023","unstructured":"Williams, P., Li, K.: Camopatch: an evolutionary strategy for generating camoflauged adversarial patches. Adv. Neural. Inf. Process. Syst. 36, 67269\u201367283 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR32","unstructured":"Wu, J., Zhou, M., Zhu, C., Liu, Y., Harandi, M., Li, L.: Performance evaluation of adversarial attacks: Discrepancies and solutions. arXiv preprint arXiv:2104.11103 (2021)"},{"issue":"16","key":"13_CR33","doi-asserted-by":"publisher","first-page":"5323","DOI":"10.3390\/s21165323","volume":"21","author":"T Wu","year":"2021","unstructured":"Wu, T., Guo, Y., Li, M., Zhang, H., Xu, C.: Extended spatially localized perturbation: Robust adversarial perturbation with camouflage patch. Sensors 21(16), 5323 (2021)","journal-title":"Sensors"},{"key":"13_CR34","unstructured":"Xu, W., Jia, X., Zhang, Y., Song, D.: A survey on physical adversarial attacks in computer vision. arXiv preprint arXiv:2209.14262 (2022)"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Ye, B., Yin, H., Yan, J., Ge, W.: Patch-based attack on traffic sign recognition. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 164\u2013171. IEEE (2021)","DOI":"10.1109\/ITSC48978.2021.9564956"},{"key":"13_CR36","volume":"125","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Hu, W., Fu, H., Zhu, F., Zhang, Z.: Visually imperceptible adversarial patch attacks. Comput. Secur. 125, 102984 (2023)","journal-title":"Comput. Secur."},{"key":"13_CR37","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","Security, Privacy, and Applied Cryptography Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16342-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T08:58:16Z","timestamp":1770800296000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16342-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163417","9783032163424"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16342-4_13","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":"12 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SPACE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security, Privacy, and Applied Cryptography Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guwahati","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"space2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.iitg.ac.in\/space2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}