Abstract：The practical effectiveness of existing adversarial attack methods for object detection in medicine is limited by the challenge of achieving high attack success rates and strong stealthiness of adversarial examples. To address this issue, adversarial attack algorithm for object detection based on local-attribute generative adversarial networks is proposed in this paper. It is intended to optimize the quality of adversarial examples and improve attack performance. First, an image is partitioned into patches to construct its graph structure, and a local attribute discrepancy loss derived from the graph is proposed to enhance the visual stealthiness of adversarial examples. Second, a target mislocalization loss is introduced to mislead the detector into producing inaccurate object localizations, thereby amplifying the adversarial impact.Finally, these two loss functions are integrated, and the generative adversarial network is updated through backpropagation. Experiments on two publicly available blood cell datasets, BCCD and LISC, demonstrate that the adversarial examples generated by the proposed method against the Faster R-CNN model outperform those by the existing algorithms in terms of attack success rate and stealthiness. Moreover, the generated adversarial examples exhibit strong attack transferability.
[1] LI J Z, SU H, ZHU J, et al. Textbook Question Answering under Instructor Guidance with Memory Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 3655-3663. [2] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 2017, 60(6): 84-90. [3] ZHAO Z Q, ZHENG P, XU S T, et al. Object Detection with Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3212-3232. [4] BOLYA D, ZHOU C, XIAO F Y, et al. YOLACT: Real-Time Instance Segmentation // Proc of the IEEE/CVF International Confe-rence on Computer Vision. Washington, USA: IEEE, 2019: 9156-9165. [5] DIPU N M, SHOHAN S A, SALAM K M A. Deep Learning Based Brain Tumor Detection and Classification // Proc of the International Conference on Intelligent Technologies. Washington, USA: IEEE, 2021. DOI: 10.1109/CONIT51480.2021.9498384. [6] YANG S, FANG B, TANG W, et al. Faster R-CNN Based Microscopic Cell Detection // Proc of the International Conference on Security, Pattern Analysis, and Cybernetics. Washington, USA: IEEE, 2017: 345-350. [7] WANG Y T, WANG K F, ZHU Z X, et al. Adversarial Attacks on Faster R-CNN Object Detector. Neurocomputing, 2020, 382: 87-95. [8] LU J J, SIBAI H, FABRY E. Adversarial Examples That Fool Detectors[C/OL]. [2025-06-27]. https://arxiv.org/pdf/1712.02494. [9] MEDGHALCHI Y, HEIDARI M, ALLARD C, et al. Prompt2Per-turb(P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2025: 28564-28574. [10] GULSOY E K, AYAS S, KABLAN E B, et al. Enhancing the Adver-sarial Robustness in Medical Image Classification: Exploring Adversarial Machine Learning with Vision Transformers-Based Mo-dels. Neural Computing and Applications, 2025, 37(12): 7971-7989. [11] GOODFELLOW I J, SHLENS J, SZEGEDY C. Explaining and Harnessing Adversarial Examples[C/OL]. [2025-06-27]. https://arxiv.org/pdf/1412.6572. [12] KURAKIN A, GOODFELLOW I J, BENGIO S. Adversarial Examples in the Physical World[C/OL]. [2025-06-27]. https://arxiv.org/pdf/1607.02533. [13] MADRY A, MAKELOV A, SCHMIDT L, et al. Towards Deep Learning Models Resistant to Adversarial Attacks[C/OL].[2025-06-27]. https://arxiv.org/pdf/1706.06083. [14] CARLINI N, WAGNER D. Towards Evaluating the Robustness of Neural Networks // Proc of the IEEE Symposium on Security and Privacy. Washington, USA: IEEE, 2017: 39-57. [15] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. AGene-rative Adversarial Nets // Proc of the 28th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2014, II: 2672-2680. [16] XIE C H, WANG J Y, ZHANG Z S, et al. Adversarial Examples for Semantic Segmentation and Object Detection // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 1378-1387. [17] CHOW K H, LIU L, LOPER M, et al. Adversarial Objectness Gradient Attacks in Real-Time Object Detection Systems // Proc of the 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications. Washington, USA: IEEE, 2020: 263-272. [18] WEI X X, LIANG S Y, CHEN N, et al. Transferable Adversarial Attacks for Image and Video Object Detection // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2019: 954-960. [19] GIRSHICK R. Fast R-CNN // Proc of the IEEE International Confe-rence on Computer Vision. Washington, USA: IEEE, 2015: 1440-1448. [20] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [21] HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 2980-2988. [22] REDMON J, DIVVALA S, GIRSHICK R, et al. You Only Look Once: Unified, Real-Time Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 779-788. [23] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single Shot Multi-Box Detector // Proc of the 14th European Conference on Computer Vision. Berlin, Germany: Springer, 2016, I: 21-37. [24] LI G P, XU Y, DING J, et al. Towards Generic and Controllable Attacks Against Object Detection. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62. DOI: 10.1109/TGRS.2024.3417958. [25] ZHANG J P, WU W B, HUANG J T, et al. Improving Adversarial Transferability via Neuron Attribution-Based Attacks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 14973-14982. [26] TYUKIN I Y, HIGHAM D J, GORBAN A N. On Adversarial Exam-ples and Stealth Attacks in Artificial Intelligence Systems // Proc of the International Joint Conference on Neural Networks. Washington, USA: IEEE, 2020. DOI: 10.1109/IJCNN48605.2020.9207472. [27] HAN K, WANG Y H, GUO J Y, et al. Vision GNN: An Image Is Worth Graph of Nodes // Proc of the 36th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2022: 8291-8303. [28] XU D F, ZHU Y K, CHOY C B, et al. Scene Graph Generation by Iterative Message Passing // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 3097-3106. [29] YANG J W, LU J S, LEE S, et al. Graph R-CNN for Scene Graph Generation // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 690-706. [30] PARIKH D, FEIN-ASHLEY J, YE T, et al. ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning // Proc of the International Conference on Multimedia Computing, Networking and Applications. Washington, USA: IEEE, 2025: 136-143. [31] ZHAO G M, GE W F, YU Y Z. GraphFPN: Graph Feature Pyramid Network for Object Detection // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 2743-2752. [32] XU H, JIANG C H, LIANG X D, et al. Spatial-Aware Graph Relation Network for Large-Scale Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 9290-9299. [33] CAMPS-VALLS G, MARSHEVA T V B, ZHOU D Y. Semi-supervised Graph-Based Hyperspectral Image Classification. IEEE Tran-sactions on Geoscience and Remote Sensing, 2007, 45(10): 3044-3054. [34] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 2004, 59(2): 167-181. [35] MOHAMED M, FAR B, GUAILY A. An Efficient Technique for White Blood Cells Nuclei Automatic Segmentation // Proc of the IEEE International Conference on Systems, Man, and Cybernetics. Washington, USA: IEEE, 2012: 220-225. [36] REZATOFIGHI S H, SOLTANIAN-ZADEH H. Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood. Computerized Medical Imaging and Graphics, 2011, 35(4): 333-343. [37] WANG G Q, YAN H Q, WEI X X. Enhancing Transferability of Adversarial Examples with Spatial Momentum // Proc of the 5th Chinese Conference on Pattern Recognition and Computer Vision. Berlin, Germany: Springer, 2022: 593-604.
2025, Vol. 38 
