{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T04:30:05Z","timestamp":1772944205391,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100018806","name":"Department of Science and Technology of Hubei Province","doi-asserted-by":"publisher","award":["Q20222704"],"award-info":[{"award-number":["Q20222704"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018911","name":"Natural Science Foundation of Xiaogan Municipality","doi-asserted-by":"publisher","award":["XGKJ2022010094"],"award-info":[{"award-number":["XGKJ2022010094"]}],"id":[{"id":"10.13039\/100018911","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["23YJAZH169"],"award-info":[{"award-number":["23YJAZH169"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012554","name":"Hubei Provincial Department of Education","doi-asserted-by":"publisher","award":["T2020017"],"award-info":[{"award-number":["T2020017"]}],"id":[{"id":"10.13039\/100012554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11265-025-01948-9","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T09:16:14Z","timestamp":1737969374000},"page":"871-886","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SIS-CAM: An Interpretability Analysis Method for the Security of Convolutional Neural Network Models Based on Image Big Data"],"prefix":"10.1007","volume":"96","author":[{"given":"Fang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7710-7752","authenticated-orcid":false,"given":"Yi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umer Sadiq","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"issue":"6","key":"1948_CR1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84\u201390.","journal-title":"Communications of the ACM"},{"key":"1948_CR2","unstructured":"Simonyan, K., &\u00a0Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"1948_CR3","doi-asserted-by":"crossref","first-page":"126329","DOI":"10.1016\/j.neucom.2023.126329","volume":"546","author":"P Zhang","year":"2023","unstructured":"Zhang, P., et al. (2023). Feature aggregation with transformer for rgb-t salient object detection. Neurocomputing, 546, 126329.","journal-title":"Neurocomputing"},{"key":"1948_CR4","doi-asserted-by":"crossref","unstructured":"Redmon, J., et\u00a0al. (2016). You only look once: Unified, real-time object detection. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2016.91"},{"key":"1948_CR5","doi-asserted-by":"crossref","unstructured":"Feng, Y.,\u00a0Vanam, S., et\u00a0al. (2023). Investigating code generation performance of chatgpt with crowdsourcing social data. In IEEE 47th Annual Computers, Software, and Applications Conf.","DOI":"10.1109\/COMPSAC57700.2023.00117"},{"key":"1948_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et\u00a0al. (2023). Communication-efficient stochastic gradient descent ascent with momentum algorithms. In IJCAI 2023.","DOI":"10.24963\/ijcai.2023\/512"},{"key":"1948_CR7","doi-asserted-by":"crossref","unstructured":"Qiu, M., &\u00a0Qiu, H. (2020). Review on image processing based adversarial example defenses in computer vision. In IEEE 6th Intl Conf. BigDataSecurity, pp. 94\u201399","DOI":"10.1109\/BigDataSecurity-HPSC-IDS49724.2020.00027"},{"key":"1948_CR8","doi-asserted-by":"crossref","first-page":"104410","DOI":"10.1016\/j.compbiomed.2021.104410","volume":"133","author":"H Saleem","year":"2021","unstructured":"Saleem, H., Shahid, A. R., & Raza, B. (2021). Visual interpretability in 3d brain tumor segmentation network. Computers in Biology and Medicine, 133, 104410.","journal-title":"Computers in Biology and Medicine"},{"key":"1948_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, B., et\u00a0al. (2016). Learning deep features for discriminative localization. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2016.319"},{"key":"1948_CR10","unstructured":"Lin, M.,\u00a0Chen, Q., &\u00a0Yan, S. (2013). Network in network. arXiv:1312.4400."},{"key":"1948_CR11","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., et\u00a0al. (2018). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. Paper presented at the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).","DOI":"10.1109\/WACV.2018.00097"},{"key":"1948_CR12","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., et\u00a0al. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. Paper presented at the IEEE International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2017.74"},{"key":"1948_CR13","unstructured":"Qi, Z.,\u00a0Khorram, S., &\u00a0Li, F. (2019). Visualizing deep networks by optimizing with integrated gradients. Paper presented at CVPR Workshops."},{"key":"1948_CR14","doi-asserted-by":"crossref","unstructured":"Kapishnikov, A., et\u00a0al. (2019). Xrai: Better attributions through regions. Paper presented at the IEEE\/CVF International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2019.00505"},{"key":"1948_CR15","unstructured":"Li, Q. (2022). Understanding saliency prediction with deep convolutional neural networks and psychophysical models. arXiv:2204.06071"},{"key":"1948_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, Q.,\u00a0Rao, L., &\u00a0Yang, Y. (2021). A novel visual interpretability for deep neural networks by optimizing activation maps with perturbation. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v35i4.16450"},{"key":"1948_CR17","doi-asserted-by":"crossref","first-page":"6050","DOI":"10.1109\/TIP.2021.3091833","volume":"30","author":"B Wang","year":"2021","unstructured":"Wang, B., et al. (2021). Multi-scale low-discriminative feature reactivation for weakly supervised object localization. IEEE Transactions on Image Processing, 30, 6050\u20136065.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1948_CR18","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","volume":"30","author":"P-T Jiang","year":"2021","unstructured":"Jiang, P.-T., et al. (2021). Layercam: Exploring hierarchical class activation maps for localization. IEEE Transactions on Image Processing, 30, 5875\u20135888.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1948_CR19","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.-A., et\u00a0al. (2020). There and back again: Revisiting backpropagation saliency methods. Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR42600.2020.00886"},{"key":"1948_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., et\u00a0al. (2020). Score-cam: Score-weighted visual explanations for convolutional neural networks. Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops.","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"1948_CR21","unstructured":"Naidu, R., et\u00a0al. (2020). Is-cam: Integrated score-cam for axiomatic-based explanations. arXiv:2010.03023"},{"issue":"1","key":"1948_CR22","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/COMST.2021.3134955","volume":"24","author":"X Wei","year":"2021","unstructured":"Wei, X., Guo, H., et al. (2021). Reliable data collection techniques in underwater wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 24(1), 404\u2013431.","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"1","key":"1948_CR23","first-page":"43","volume":"4","author":"M Qiu","year":"2006","unstructured":"Qiu, M., Zhang, K., & Huang, M. (2006). Usability in mobile interface browsing. Web Intelligence and Agent Systems, 4(1), 43\u201359.","journal-title":"Web Intelligence and Agent Systems"},{"key":"1948_CR24","unstructured":"Qiu, M.,\u00a0Zhang, K., &\u00a0Huang, M. (2004). An empirical study of web interface design on small display devices. In IEEE\/WIC\/ACM Intl. Conf. on Web Intelligence (WI\u201904)."},{"issue":"1","key":"1948_CR25","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1109\/TCC.2016.2607708","volume":"7","author":"M Qiu","year":"2016","unstructured":"Qiu, M., Dai, W., & Vasilakos, A. (2016). Loop parallelism maximization for multimedia data processing in mobile vehicular clouds. IEEE Transactions on Cloud Computing, 7(1), 250\u2013258.","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"1948_CR26","doi-asserted-by":"crossref","unstructured":"Gao, Y.,\u00a0Iqbal, S., et\u00a0al. (2015). Performance and power analysis of high-density multi-gpgpu architectures: A preliminary case study. In IEEE 17th HPCC conf., pp. 66\u201371.","DOI":"10.1109\/HPCC-CSS-ICESS.2015.68"},{"issue":"5","key":"1948_CR27","first-page":"445","volume":"54","author":"Z Shao","year":"2007","unstructured":"Shao, Z., Wang, M., et al. (2007). Real-time dynamic voltage loop scheduling for multi-core embedded systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 54(5), 445\u2013449.","journal-title":"IEEE Transactions on Circuits and Systems II: Express Briefs"},{"key":"1948_CR28","doi-asserted-by":"crossref","unstructured":"Zeng, C., et al. (2023). Abs-cam: a gradient optimization interpretable approach for explanation of convolutional neural networks. Signal, Image and Video Processing, 17(4), 1069\u20131076.","DOI":"10.1007\/s11760-022-02313-0"},{"key":"1948_CR29","unstructured":"Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. Paper presented at Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014. Proceedings, Part I 13."},{"key":"1948_CR30","doi-asserted-by":"crossref","unstructured":"Fong, R. C., &\u00a0Vedaldi, A. (2017). Interpretable explanations of black boxes by meaningful perturbation. Paper presented at the IEEE International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2017.371"},{"key":"1948_CR31","unstructured":"Agarwal, C.,\u00a0Schonfeld, D., &\u00a0Nguyen, A. (2019). Removing input features via a generative model to explain their attributions to classifier\u2019s decisions."},{"key":"1948_CR32","unstructured":"Sundararajan, M.,\u00a0Taly, A., &\u00a0Yan, Q. (2017). Axiomatic attribution for deep networks. Paper presented at the International Conference on Machine Learning."},{"key":"1948_CR33","unstructured":"Petsiuk, V.,\u00a0Das, A., &\u00a0Saenko, K. (2018). Rise: Randomized input sampling for explanation of black-box models. arXiv:1806.07421"},{"key":"1948_CR34","unstructured":"Simonyan, K.,\u00a0Vedaldi, A., &\u00a0Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034"},{"issue":"7","key":"1948_CR35","doi-asserted-by":"crossref","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., et al. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One, 10(7), e0130140.","journal-title":"PloS One"},{"key":"1948_CR36","doi-asserted-by":"crossref","unstructured":"Nam, W.-J., et\u00a0al. (2020). Relative attributing propagation: Interpreting the comparative contributions of individual units in deep neural networks. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v34i03.5632"},{"key":"1948_CR37","unstructured":"Smilkov, D., et\u00a0al. (2017). Smoothgrad: removing noise by adding noise. arXiv:1706.03825"},{"key":"1948_CR38","doi-asserted-by":"crossref","unstructured":"Lee, K. H., et\u00a0al. (2021). Lfi-cam: Learning feature importance for better visual explanation. Paper presented at the Proceedings of the IEEE\/CVF International Conference on Computer Vision.","DOI":"10.1109\/ICCV48922.2021.00139"},{"key":"1948_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Q.,\u00a0Rao, L., &\u00a0Yang, Y. (2021). Group-cam: Group score-weighted visual explanations for deep convolutional networks. arXiv:2103.13859","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"1948_CR40","doi-asserted-by":"crossref","unstructured":"Lee, J. R., et\u00a0al. (2021). Relevance-cam: Your model already knows where to look. Paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR46437.2021.01470"},{"key":"1948_CR41","unstructured":"Englebert, A.,\u00a0Cornu, O., &\u00a0De Vleeschouwer, C. (2022). Poly-cam: High resolution class activation map for convolutional neural networks. arXiv:2204.13359"},{"key":"1948_CR42","doi-asserted-by":"crossref","unstructured":"Li, H., et\u00a0al. (2022). Fd-cam: Improving faithfulness and discriminability of visual explanation for cnns. Paper presented at the 2022 26th International Conference on Pattern Recognition (ICPR).","DOI":"10.1109\/ICPR56361.2022.9956466"},{"key":"1948_CR43","unstructured":"Adebayo, J., et al. (2018). Sanity checks for saliency maps. Advances in Neural Information Processing Systems, 31."}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-025-01948-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-025-01948-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-025-01948-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T23:20:02Z","timestamp":1743376802000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-025-01948-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":43,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1948"],"URL":"https:\/\/doi.org\/10.1007\/s11265-025-01948-9","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"10 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"For this type of study formal consent was not required. This manuscript does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors have no Conflicts of Interest to declare for this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}