{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:24:15Z","timestamp":1783052655617,"version":"3.54.6"},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2016"],"award-info":[{"award-number":["U21B2016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272145"],"award-info":[{"award-number":["62272145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.eswa.2026.132926","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T16:17:54Z","timestamp":1779466674000},"page":"132926","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TIAFuzz: Transferable fuzzing via distillation for image-based deep learning systems"],"prefix":"10.1016","volume":"329","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5656-0766","authenticated-orcid":false,"given":"Yunhe","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-5795","authenticated-orcid":false,"given":"Shunhui","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7033-5688","authenticated-orcid":false,"given":"Hai","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2563-083X","authenticated-orcid":false,"given":"Yan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2800-3306","authenticated-orcid":false,"given":"Mingxuan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-408X","authenticated-orcid":false,"given":"Pengcheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.eswa.2026.132926_bib0001","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","article-title":"Deep learning models for digital image processing: A review","volume":"57","author":"Archana","year":"2024","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2026.132926_bib0002","series-title":"2017\u202fIEEE Symposium on security and privacy (sp)","first-page":"39","article-title":"Towards evaluating the robustness of neural networks","author":"Carlini","year":"2017"},{"key":"10.1016\/j.eswa.2026.132926_bib0003","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4489","article-title":"An adaptive model ensemble adversarial attack for boosting adversarial transferability","author":"Chen","year":"2023"},{"key":"10.1016\/j.eswa.2026.132926_bib0004","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv: 1706.05587."},{"key":"10.1016\/j.eswa.2026.132926_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128743","article-title":"Deeply understanding features to achieve efficient remote sensing image classification","volume":"295","author":"Chen","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_sbref0006","article-title":"Enhancing adversarial transferability through frequency-domain boundary samples tuning","volume":"298","author":"Cheng","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_sbref0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114469","article-title":"Dmfp: Dynamic multiscale feature perturbations for transferable adversarial attacks","volume":"330","author":"Cheng","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132926_bib0008","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"6437","article-title":"Sparse-rs: A versatile framework for query-efficient sparse black-box adversarial attacks","volume":"Vol. 36","author":"Croce","year":"2022"},{"key":"10.1016\/j.eswa.2026.132926_bib0009","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4724","article-title":"Sparse and imperceivable adversarial attacks","author":"Croce","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0010","series-title":"2020\u202fIEEE International conference on image processing (ICIP)","first-page":"648","article-title":"Substitute model generation for black-box adversarial attack based on knowledge distillation","author":"Cui","year":"2020"},{"key":"10.1016\/j.eswa.2026.132926_bib0011","doi-asserted-by":"crossref","unstructured":"Deng, J., Palmer, A., Mahmood, R., Rathbun, E., Bi, J., Mahmood, K., & Aguiar, D. (2024). Distilling adversarial robustness using heterogeneous teachers. arXiv: 2402.15586.","DOI":"10.1016\/j.procs.2025.07.114"},{"key":"10.1016\/j.eswa.2026.132926_bib0012","first-page":"11226","article-title":"Greedyfool: Distortion-aware sparse adversarial attack","volume":"33","author":"Dong","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132926_bib0013","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"9185","article-title":"Boosting adversarial attacks with momentum","author":"Dong","year":"2018"},{"key":"10.1016\/j.eswa.2026.132926_bib0014","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4312","article-title":"Evading defenses to transferable adversarial examples by translation-invariant attacks","author":"Dong","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0015","series-title":"Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXII 16","first-page":"35","article-title":"Sparse adversarial attack via perturbation factorization","author":"Fan","year":"2020"},{"key":"10.1016\/j.eswa.2026.132926_bib0016","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"571","article-title":"Learning to learn transferable attack","volume":"Vol. 36","author":"Fang","year":"2022"},{"key":"10.1016\/j.eswa.2026.132926_bib0017","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv: 1412.6572."},{"key":"10.1016\/j.eswa.2026.132926_sbref0018","article-title":"Alda: Enhancing the transferability of adversarial attacks with attention-guided look-ahead and data augmentation","volume":"172","author":"Guo","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.132926_bib0019","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.eswa.2026.132926_sbref0020","article-title":"Hmambaocc: Hierarchical mamba for occupancy flow field prediction in autonomous driving under mixed traffic environments","volume":"299","author":"He","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_bib0021","unstructured":"He, Z., Wang, W., Dong, J., & Tan, T. (2021). Transferable sparse adversarial attack. arXiv: 2105.14727."},{"key":"10.1016\/j.eswa.2026.132926_bib0022","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"14963","article-title":"Transferable sparse adversarial attack","author":"He","year":"2022"},{"key":"10.1016\/j.eswa.2026.132926_bib0023","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv: 1503.02531."},{"issue":"1","key":"10.1016\/j.eswa.2026.132926_bib0024","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/(SICI)1099-1689(199903)9:1<3::AID-STVR169>3.0.CO;2-Z","article-title":"Boundary values and automated component testing","volume":"9","author":"Hoffman","year":"1999","journal-title":"Software Testing, Verification and Reliability"},{"key":"10.1016\/j.eswa.2026.132926_bib0025","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"1314","article-title":"Searching for mobilenetv3","author":"Howard","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0026","unstructured":"Howard, A. G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv: 1704.04861."},{"key":"10.1016\/j.eswa.2026.132926_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2026.131677","article-title":"Dynamic gradient fusion method with local spatial and multi-scale frequency transformations for transferable adversarial attacks","volume":"314","author":"Hu","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_bib0028","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.eswa.2026.132926_bib0029","unstructured":"Imtiaz, T., Kohler, M., Miller, J., Wang, Z., Sznaier, M., Camps, O., & Dy, J. (2022). Saif: Sparse adversarial and interpretable attack framework. arXiv: 2212.07495."},{"issue":"9","key":"10.1016\/j.eswa.2026.132926_bib0030","first-page":"4003","article-title":"Adversarial example generation method based on sparse perturbation","volume":"34","author":"Ji","year":"2023","journal-title":"Journal of Software"},{"key":"10.1016\/j.eswa.2026.132926_sbref0031","article-title":"A prototype-based framework for open-set heterogeneous federated face recognition","volume":"299","author":"Kim","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_bib0032","unstructured":"Krizhevsky, A., Hinton, G. et al. (2009). Learning multiple layers of features from tiny imagesUniversity of Toronto2009https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdfhttps:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf."},{"key":"10.1016\/j.eswa.2026.132926_bib0033","series-title":"Artificial intelligence safety and security","first-page":"99","article-title":"Adversarial examples in the physical world","author":"Kurakin","year":"2018"},{"key":"10.1016\/j.eswa.2026.132926_bib0034","unstructured":"Laine, S., & Aila, T. (2016). Temporal ensembling for semi-supervised learning. arXiv: 1610.02242."},{"key":"10.1016\/j.eswa.2026.132926_bib0035","series-title":"Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering","first-page":"475","article-title":"Fairfuzz: A targeted mutation strategy for increasing greybox fuzz testing coverage","author":"Lemieux","year":"2018"},{"key":"10.1016\/j.eswa.2026.132926_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108979","article-title":"Adaptive momentum variance for attention-guided sparse adversarial attacks","volume":"133","author":"Li","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.132926_sbref0037","article-title":"Attention-driven feature enhancement network for object detection","volume":"669","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132926_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109849","article-title":"Few pixels attacks with generative model","volume":"144","author":"Li","year":"2023","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.1016\/j.eswa.2026.132926_bib0039","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/18.61115","article-title":"Divergence measures based on the shannon entropy","volume":"37","author":"Lin","year":"2002","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.1016\/j.eswa.2026.132926_bib0040","unstructured":"Lin, J., Song, C., He, K., Wang, L., & Hopcroft, J. E. (2019). Nesterov accelerated gradient and scale invariance for adversarial attacks. arXiv: 1908.06281."},{"key":"10.1016\/j.eswa.2026.132926_bib0041","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.eswa.2026.132926_bib0042","series-title":"Icassp 2025-2025 IEEE international conference on acoustics, speech and signal processing (ICASSP)","first-page":"1","article-title":"Boosting the transferability of adversarial examples via local mixup and adaptive step size","author":"Liu","year":"2025"},{"key":"10.1016\/j.eswa.2026.132926_bib0043","series-title":"Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, the Netherlands, October 11\u201314, 2016, Proceedings, Part I 14","first-page":"21","article-title":"Ssd: Single shot multibox detector","author":"Liu","year":"2016"},{"key":"10.1016\/j.eswa.2026.132926_bib0044","unstructured":"Liu, Y., Chen, X., Liu, C., & Song, D. (2016b). Delving into transferable adversarial examples and black-box attacks. arXiv: 1611.02770."},{"key":"10.1016\/j.eswa.2026.132926_bib0045","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.eswa.2026.132926_bib0046","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.eswa.2026.132926_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125456","article-title":"Beyond low-dimensional features: Enhancing semi-supervised medical image semantic segmentation with advanced consistency learning techniques","volume":"261","author":"Lu","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132926_bib0048","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.inpa.2023.02.001","article-title":"Semantic segmentation of agricultural images: A survey","volume":"11","author":"Luo","year":"2024","journal-title":"Information Processing in Agriculture"},{"key":"10.1016\/j.eswa.2026.132926_bib0049","series-title":"Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering","first-page":"120","article-title":"Deepgauge: Multi-granularity testing criteria for deep learning systems","author":"Ma","year":"2018"},{"key":"10.1016\/j.eswa.2026.132926_bib0050","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv: 1706.06083."},{"key":"10.1016\/j.eswa.2026.132926_bib0051","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"24696","article-title":"Transferable structural sparse adversarial attack via exact group sparsity training","author":"Ming","year":"2024"},{"key":"10.1016\/j.eswa.2026.132926_bib0052","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9087","article-title":"Sparsefool: A few pixels make a big difference","author":"Modas","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0053","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"2574","article-title":"Deepfool: A simple and accurate method to fool deep neural networks","author":"Moosavi-Dezfooli","year":"2016"},{"issue":"1","key":"10.1016\/j.eswa.2026.132926_bib0054","doi-asserted-by":"crossref","first-page":"13","DOI":"10.20982\/tqmp.04.1.p013","article-title":"The mann-whitney u: A test for assessing whether two independent samples come from the same distribution","volume":"4","author":"Nachar","year":"2008","journal-title":"Tutorials in Quantitative Methods for Psychology"},{"key":"10.1016\/j.eswa.2026.132926_bib0055","unstructured":"Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., & Brox, T. (2019). Self: Learning to filter noisy labels with self-ensembling. arXiv: 1910.01842."},{"key":"10.1016\/j.eswa.2026.132926_bib0056","series-title":"2008 Sixth indian conference on computer vision, graphics & image processing","first-page":"722","article-title":"Automated flower classification over a large number of classes","author":"Nilsback","year":"2008"},{"key":"10.1016\/j.eswa.2026.132926_bib0057","series-title":"Proceedings of the 2017\u202fACM on Asia conference on computer and communications security","first-page":"506","article-title":"Practical black-box attacks against machine learning","author":"Papernot","year":"2017"},{"key":"10.1016\/j.eswa.2026.132926_bib0058","series-title":"2016\u202fIEEE European symposium on security and privacy (Euros&p)","first-page":"372","article-title":"The limitations of deep learning in adversarial settings","author":"Papernot","year":"2016"},{"issue":"5","key":"10.1016\/j.eswa.2026.132926_bib0059","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0041-5553(64)90137-5","article-title":"Some methods of speeding up the convergence of iteration methods","volume":"4","author":"Polyak","year":"1964","journal-title":"Ussr Computational Mathematics and Mathematical Physics"},{"key":"10.1016\/j.eswa.2026.132926_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129436","article-title":"Space-constrained random sparse adversarial attack","volume":"623","author":"Qin","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132926_bib0061","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"10428","article-title":"Designing network design spaces","author":"Radosavovic","year":"2020"},{"issue":"6","key":"10.1016\/j.eswa.2026.132926_bib0062","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"10.1016\/j.eswa.2026.132926_bib0063","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556."},{"key":"10.1016\/j.eswa.2026.132926_bib0064","unstructured":"Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., & Beyer, L. (2021). How to train your vit? Data, augmentation, and regularization in vision transformers. arXiv: 2106.10270."},{"issue":"5","key":"10.1016\/j.eswa.2026.132926_bib0065","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","article-title":"One pixel attack for fooling deep neural networks","volume":"23","author":"Su","year":"2019","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"10.1016\/j.eswa.2026.132926_bib0066","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.eswa.2026.132926_bib0067","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv: 1312.6199."},{"key":"10.1016\/j.eswa.2026.132926_bib0068","series-title":"International conference on machine learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0069","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"Tarvainen","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132926_bib0070","series-title":"Detect and repair errors for DNN-based software","author":"Tian","year":"2021"},{"issue":"2","key":"10.1016\/j.eswa.2026.132926_bib0071","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/TAI.2022.3168038","article-title":"Imperceptible and sparse adversarial attacks via a dual-population-based constrained evolutionary algorithm","volume":"4","author":"Tian","year":"2022","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132926_bib0072","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"9627","article-title":"Fcos: Fully convolutional one-stage object detection","author":"Tian","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0073","first-page":"24261","article-title":"Mlp-mixer: An all-mlp architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"35","key":"10.1016\/j.eswa.2026.132926_bib0074","doi-asserted-by":"crossref","first-page":"83535","DOI":"10.1007\/s11042-024-18872-y","article-title":"Yolo-based object detection models: A review and its applications","volume":"83","author":"Vijayakumar","year":"2024","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.eswa.2026.132926_bib0075","doi-asserted-by":"crossref","first-page":"12080","DOI":"10.1109\/TIFS.2025.3630890","article-title":"Greedypixel: Fine-grained black-box adversarial attack via greedy algorithm","volume":"20","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"1","key":"10.1016\/j.eswa.2026.132926_bib0076","first-page":"1","article-title":"Context-aware fuzzing for robustness enhancement of deep learning models","volume":"34","author":"Wang","year":"2024","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"10.1016\/j.eswa.2026.132926_bib0077","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"1924","article-title":"Enhancing the transferability of adversarial attacks through variance tuning","author":"Wang","year":"2021"},{"key":"10.1016\/j.eswa.2026.132926_bib0078","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"12291","article-title":"Black-box sparse adversarial attack via multi-objective optimisation","author":"Williams","year":"2023"},{"key":"10.1016\/j.eswa.2026.132926_sbref0079","article-title":"Density-guided two-stage small object detection in UAV images","volume":"297","author":"Xie","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132926_bib0080","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"2730","article-title":"Improving transferability of adversarial examples with input diversity","author":"Xie","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0081","series-title":"Proceedings of the 28th ACM SIGSOFT international symposium on software testing and analysis","first-page":"146","article-title":"Deephunter: A coverage-guided fuzz testing framework for deep neural networks","author":"Xie","year":"2019"},{"key":"10.1016\/j.eswa.2026.132926_bib0082","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4119","article-title":"Towards effective adversarial textured 3d meshes on physical face recognition","author":"Yang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132926_bib0083","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TMM.2021.3115635","article-title":"Tc-net: Detecting noisy labels via transform consistency","volume":"24","author":"Yi","year":"2021","journal-title":"IEEE Transactions on Multimedia"},{"issue":"11","key":"10.1016\/j.eswa.2026.132926_bib0084","doi-asserted-by":"crossref","first-page":"4630","DOI":"10.1109\/TSE.2021.3124006","article-title":"Cagfuzz: Coverage-guided adversarial generative fuzzing testing for image-based deep learning systems","volume":"48","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10.1016\/j.eswa.2026.132926_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114079","article-title":"Improving adversarial transferability via adaptive ensemble attack with post-optimization","volume":"326","author":"Zhang","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132926_bib0086","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122836","article-title":"Autonomous driving system: A comprehensive survey","volume":"242","author":"Zhao","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"10.1016\/j.eswa.2026.132926_bib0087","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1109\/TPAMI.2025.3630185","article-title":"Sparse-PGD: A unified framework for sparse adversarial perturbations generation","volume":"48","author":"Zhong","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132926_bib0088","series-title":"International conference on machine learning","first-page":"12868","article-title":"Sparse and imperceptible adversarial attack via a homotopy algorithm","author":"Zhu","year":"2021"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018385?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426018385?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:14:50Z","timestamp":1783052090000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426018385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":88,"alternative-id":["S0957417426018385"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132926","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TIAFuzz: Transferable fuzzing via distillation for image-based deep learning systems","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132926","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132926"}}