Publications
Citation summary (September 2026): 760+ citations, h-index 11, i10-index 11. See Google Scholar for current metrics.
Journal Papers
- 2026Ma, J., Enan, A., Cheng, L., & Chowdhury, M. (2026). Understanding the Risks of Asphalt Art to the Reliability of Vision-Based Perception Systems. Transportation Research Record. In press. https://arxiv.org/pdf/2508.02530
@article{ma2026asphalt, title = {Understanding the Risks of Asphalt Art to the Reliability of Vision-Based Perception Systems}, author = {Ma, Jin and Enan, Abyad and Cheng, Long and Chowdhury, Mashrur}, journal = {Transportation Research Record}, note = {In press}, year = {2026}, url = {https://arxiv.org/pdf/2508.02530} }We investigate the impact of asphalt art on the reliability of surveillance perception systems.
- 2026Aftabi, N., Samaha, P., Ma, J., Cheng, L., Harik, R., & Li, D. (2026). ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations. Journal of Manufacturing Systems, 89, 504–523. https://arxiv.org/pdf/2509.10948
@article{aftabi2026vistr, title = {ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations}, author = {Aftabi, N. and Samaha, P. and Ma, Jin and Cheng, Long and Harik, R. and Li, D.}, journal = {Journal of Manufacturing Systems}, volume = {89}, pages = {504--523}, year = {2026}, doi = {10.1016/j.jmsy.2026.09.008}, url = {https://arxiv.org/pdf/2509.10948} } - 2024Li, J., Xu, R., Liu, X., Ma, J., Li, B., Zou, Q., Ma, J., & Yu, H. (2024). Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather. IEEE Transactions on Intelligent Vehicles, 10(2), 900–911. https://arxiv.org/pdf/2307.09676
@article{li2024domain, title = {Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather}, author = {Li, J. and Xu, R. and Liu, X. and Ma, J. and Li, B. and Zou, Q. and Ma, J. and Yu, H.}, journal = {IEEE Transactions on Intelligent Vehicles}, volume = {10}, number = {2}, pages = {900--911}, year = {2024}, doi = {10.1109/TIV.2024.3419689}, url = {https://arxiv.org/pdf/2307.09676} } - 2023Liu, X., Li, J., Ma, J., Sun, H., Xu, Z., Zhang, T., & Yu, H. (2023). Deep transfer learning for intelligent vehicle perception: A survey. Green Energy and Intelligent Transportation, 2(5), 100125. https://arxiv.org/pdf/2306.15110
@article{liu2023deep, title = {Deep transfer learning for intelligent vehicle perception: A survey}, author = {Liu, Xinyu and Li, Jinlong and Ma, Jin and Sun, Huiming and Xu, Zhigang and Zhang, Tianyun and Yu, Hongkai}, journal = {Green Energy and Intelligent Transportation}, volume = {2}, number = {5}, pages = {100125}, year = {2023}, doi = {10.1016/j.geits.2023.100125}, url = {https://arxiv.org/pdf/2306.15110} } - 2023Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H. (2023). Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication. IEEE Transactions on Intelligent Vehicles, 8(4), 2650–2660. https://arxiv.org/pdf/2212.08273
@article{li2023cooperative, title = {Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication}, author = {Li, J. and Xu, R. and Liu, X. and Ma, J. and Chi, Z. and Ma, J. and Yu, H.}, journal = {IEEE Transactions on Intelligent Vehicles}, volume = {8}, number = {4}, pages = {2650--2660}, year = {2023}, doi = {10.1109/TIV.2023.3260040}, url = {https://arxiv.org/pdf/2212.08273} } - 2023Sun, H., Ma, J., Guo, Q., Zou, Q., Song, S., Lin, Y., & Yu, H. (2023). Coarse-to-Fine Task-Driven Inpainting for Geoscience Images. IEEE Transactions on Circuits and Systems for Video Technology, 33(12), 7170–7182. https://arxiv.org/pdf/2211.11059
@article{sun2023inpainting, title = {Coarse-to-Fine Task-Driven Inpainting for Geoscience Images}, author = {Sun, H. and Ma, Jin and Guo, Q. and Zou, Q. and Song, S. and Lin, Y. and Yu, H.}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {33}, number = {12}, pages = {7170--7182}, year = {2023}, doi = {10.1109/TCSVT.2023.3276719}, url = {https://arxiv.org/pdf/2211.11059} } - 2018Pang, S., Ma, J., Xue, J., Zhu, J., & Ordonez, V. (2018). Deep Feature Aggregation and Image Re-Ranking with Heat Diffusion for Image Retrieval. IEEE Transactions on Multimedia, 21(6), 1513–1523. https://arxiv.org/pdf/1805.08587
@article{pang2018heat, title = {Deep Feature Aggregation and Image Re-Ranking with Heat Diffusion for Image Retrieval}, author = {Pang, Shanmin and Ma, Jin and Xue, J. and Zhu, Jihua and Ordonez, V.}, journal = {IEEE Transactions on Multimedia}, volume = {21}, number = {6}, pages = {1513--1523}, year = {2018}, doi = {10.1109/TMM.2018.2876833}, url = {https://arxiv.org/pdf/1805.08587} } - 2018Pang, S., Ma, J., Zhu, J., Xue, J., & Tian, Q. (2018). Improving Object Retrieval Quality by Integration of Similarity Propagation and Query Expansion. IEEE Transactions on Multimedia, 21(3), 760–770.
@article{pang2018retrieval, title = {Improving Object Retrieval Quality by Integration of Similarity Propagation and Query Expansion}, author = {Pang, Shanmin and Ma, Jin and Zhu, Jihua and Xue, J. and Tian, Q.}, journal = {IEEE Transactions on Multimedia}, volume = {21}, number = {3}, pages = {760--770}, year = {2018}, doi = {10.1109/TMM.2018.2866230} }
Conference Papers
- 2025Wang, F., Sun, F., Fan, M., Zhou, J., Ma, J., Chen, C., Shu, J., & Zhang, L. Y. (2025). FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments. 2025 Annual Computer Security Applications Conference (ACSAC), 411–424. https://arxiv.org/pdf/2511.02176
@inproceedings{wang2025flame, title = {FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments}, author = {Wang, Fuyi and Sun, Fangyuan and Fan, Mingyuan and Zhou, Jianying and Ma, Jin and Chen, Chao and Shu, Jiangang and Zhang, Leo Yu}, booktitle = {2025 Annual Computer Security Applications Conference (ACSAC)}, pages = {411--424}, year = {2025}, doi = {10.1109/ACSAC67867.2025.00044}, url = {https://arxiv.org/pdf/2511.02176} } - 2025Yan, J., Liao, S., Ma, J., Aldeen, M., Kumar, S., & Cheng, L. (2025). No Way to Sign Out? Unpacking Non-Compliance with Google Play’s App Account Deletion Requirements. 34th USENIX Security Symposium (USENIX Security 25), 3277–3296. https://www.usenix.org/system/files/usenixsecurity25-yan-jingwen.pdf
@inproceedings{yan2025no, title = {No Way to Sign Out? Unpacking Non-Compliance with Google Play's App Account Deletion Requirements}, author = {Yan, Jingwen and Liao, Song and Ma, Jin and Aldeen, Mohammed and Kumar, Salish and Cheng, Long}, booktitle = {34th USENIX Security Symposium (USENIX Security 25)}, pages = {3277--3296}, year = {2025}, url = {https://www.usenix.org/system/files/usenixsecurity25-yan-jingwen.pdf} }We conducted the first study investigating non-compliance issues with Google Play’s app account deletion requirements.
- 2024Aldeen, M. S., MohajerAnsari, P., Ma, J., Chowdhury, M., Cheng, L., & Pesé, M. D. (2024). An Initial Exploration of Employing Large Multimodal Models in Defending against Autonomous Vehicles Attacks. 2024 IEEE Intelligent Vehicles Symposium (IV), 3334–3341. https://mpese.com/publication/aldeen-2024-initial/IV_LMM_Paper.pdf
@inproceedings{aldeen2024defending, title = {An Initial Exploration of Employing Large Multimodal Models in Defending against Autonomous Vehicles Attacks}, author = {Aldeen, Mohammed Shujaa and MohajerAnsari, P. and Ma, Jin and Chowdhury, Mashrur and Cheng, Long and Pesé, Mert D.}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, pages = {3334--3341}, year = {2024}, doi = {10.1109/IV55156.2024.10588595}, url = {https://mpese.com/publication/aldeen-2024-initial/IV_LMM_Paper.pdf} } - 2024Aldeen, M. S., MohajerAnsari, P., Ma, J., Chowdhury, M., Cheng, L., & Pesé, M. D. (2024). WIP: A First Look at Employing Large Multimodal Models against Autonomous Vehicle Attacks. ISOC Symposium on Vehicle Security and Privacy (VehicleSec). https://mpese.com/publication/aldeen-2024-wip/vehiclesec2024-44-paper.pdf
@inproceedings{aldeen2024wip, title = {WIP: A First Look at Employing Large Multimodal Models against Autonomous Vehicle Attacks}, author = {Aldeen, Mohammed Shujaa and MohajerAnsari, P. and Ma, Jin and Chowdhury, Mashrur and Cheng, Long and Pesé, Mert D.}, booktitle = {ISOC Symposium on Vehicle Security and Privacy (VehicleSec)}, year = {2024}, doi = {10.14722/vehiclesec.2024.23044}, url = {https://mpese.com/publication/aldeen-2024-wip/vehiclesec2024-44-paper.pdf} } - 2023Li, J., Xu, R., Ma, J., Zou, Q., Ma, J., & Yu, H. (2023). Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 612–622. https://openaccess.thecvf.com/content/WACV2023/papers/Li_Domain_Adaptive_Object_Detection_for_Autonomous_Driving_Under_Foggy_Weather_WACV_2023_paper.pdf
@inproceedings{li2023foggy, title = {Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather}, author = {Li, J. and Xu, R. and Ma, J. and Zou, Q. and Ma, J. and Yu, H.}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, pages = {612--622}, year = {2023}, doi = {10.1109/WACV56688.2023.00068}, url = {https://openaccess.thecvf.com/content/WACV2023/papers/Li_Domain_Adaptive_Object_Detection_for_Autonomous_Driving_Under_Foggy_Weather_WACV_2023_paper.pdf} } - 2020Ma, J., Pang, S., Yang, B., Zhu, J., & Li, Y. (2020). Spatial-content image search in complex scenes. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2503–2511. https://openaccess.thecvf.com/content_WACV_2020/papers/Ma_Spatial-Content_Image_Search_in_Complex_Scenes_WACV_2020_paper.pdf
@inproceedings{ma2020spatial, title = {Spatial-content image search in complex scenes}, author = {Ma, Jin and Pang, Shanmin and Yang, Bo and Zhu, Jihua and Li, Yaochen}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, pages = {2503--2511}, year = {2020}, doi = {10.1109/WACV45572.2020.9093427}, url = {https://openaccess.thecvf.com/content_WACV_2020/papers/Ma_Spatial-Content_Image_Search_in_Complex_Scenes_WACV_2020_paper.pdf} }We develop a novel visually similar spatial-semantic method, namely spatial-content image search.
Workshop Papers
- 2026Rudharraju, A., Cheng, E., Tong, W., & Ma, J. (2026). Can AI Be a Doctor? Benchmarking Large Vision-Language Models on Ophthalmic Tasks. IEEE BIBM 2026 Undergraduate and High School Symposium (UGHS 2026). Accepted.
@inproceedings{rudharraju2026doctor, title = {Can AI Be a Doctor? Benchmarking Large Vision-Language Models on Ophthalmic Tasks}, author = {Rudharraju, Anvitaa and Cheng, E. and Tong, W. and Ma, Jin}, booktitle = {IEEE BIBM 2026 Undergraduate and High School Symposium (UGHS 2026)}, keywords = {workshop}, note = {Accepted}, year = {2026} } - 2025Han, X., Ma, J., Zhang, J., Liu, K., & Luo, F. (2025). Understanding the Constraints of RAG-Based Medical LVLMs: A Case Study in Ophthalmic Report Generation. 2025 IEEE International Conference on Data Mining Workshops (ICDMW), 96–102.
@inproceedings{han2025understanding, title = {Understanding the Constraints of RAG-Based Medical LVLMs: A Case Study in Ophthalmic Report Generation}, author = {Han, Xiaoyan and Ma, Jin and Zhang, Jinghan and Liu, Kunpeng and Luo, Feng}, booktitle = {2025 IEEE International Conference on Data Mining Workshops (ICDMW)}, keywords = {workshop}, pages = {96--102}, year = {2025}, doi = {10.1109/ICDMW69685.2025.00017} }Medical Large Vision-Language Models (Med-LVLMs) offer substantial potential to advance disease diagnosis by integrating visual medical data with textual clinical knowledge. However, they still face significant challenges with factual hallucination because their knowledge is limited to what was encoded during training. While Retrieval-Augmented Generation (RAG) has emerged as a promising solution, its effectiveness is highly dependent on the quality of retrieved documents, where irrelevant or noisy information can compromise the reliability of generated responses.
- 2025Ma, J., Aldeen, M. S., Luo, F., & Cheng, L. (2025). Few-Shot Detection of Hate Videos Using Multi-Modal Large Language Models. Proceedings of the 1st ACM Workshop on Deepfake, Deception, and Disinformation Security, 32–35. https://dl.acm.org/doi/pdf/10.1145/3733813.3764370
@inproceedings{ma2025few, title = {Few-Shot Detection of Hate Videos Using Multi-Modal Large Language Models}, author = {Ma, Jin and Aldeen, Mohammed Shujaa and Luo, Feng and Cheng, Long}, booktitle = {Proceedings of the 1st ACM Workshop on Deepfake, Deception, and Disinformation Security}, keywords = {workshop}, pages = {32--35}, year = {2025}, doi = {10.1145/3733813.3764370}, url = {https://dl.acm.org/doi/pdf/10.1145/3733813.3764370} }We present a method for hate video detection using multi-modal large language models (MLLMs), without time-consuming training.
- 2024Aldeen, M., Silimkhan, P., Anderson, E., Kavuru, T., Chang, T., Ma, J., Luo, F., Hu, H., & Cheng, L. (2024). An Integrated Platform for Online Abuse Research. 5th International Workshop on Cyber Social Threats (CySoc). https://workshop-proceedings.icwsm.org/pdf/2024_10.pdf
@inproceedings{aldeen2024platform, title = {An Integrated Platform for Online Abuse Research}, author = {Aldeen, Mohammed and Silimkhan, P. and Anderson, Ethan and Kavuru, Taran and Chang, T. and Ma, Jin and Luo, Feng and Hu, H. and Cheng, Long}, booktitle = {5th International Workshop on Cyber Social Threats (CySoc)}, keywords = {workshop}, year = {2024}, doi = {10.36190/2024.10}, url = {https://workshop-proceedings.icwsm.org/pdf/2024_10.pdf} } - 2021Hatay, E., Ma, J., Sun, H., Fang, J., Gao, Z., & Yu, H. (2021). Learning to Detect Phone-Related Pedestrian Distracted Behaviors with Synthetic Data. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://openaccess.thecvf.com/content/CVPR2021W/JRDB/papers/Hatay_Learning_To_Detect_Phone-Related_Pedestrian_Distracted_Behaviors_With_Synthetic_Data_CVPRW_2021_paper.pdf
@inproceedings{hatay2021pedestrian, title = {Learning to Detect Phone-Related Pedestrian Distracted Behaviors with Synthetic Data}, author = {Hatay, E. and Ma, Jin and Sun, H. and Fang, J. and Gao, Z. and Yu, H.}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, keywords = {workshop}, year = {2021}, doi = {10.1109/CVPRW53098.2021.00333}, url = {https://openaccess.thecvf.com/content/CVPR2021W/JRDB/papers/Hatay_Learning_To_Detect_Phone-Related_Pedestrian_Distracted_Behaviors_With_Synthetic_Data_CVPRW_2021_paper.pdf} }
Preprints
- 2026Ma, J., Yan, J., Aldeen, M., Anderson, E., Kavuru, T., Park, J. K., Luo, F., & Cheng, L. (2026). XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception. arXiv:2603.22453; Under review. https://arxiv.org/pdf/2603.22453
@unpublished{ma2026xnote, title = {XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception}, author = {Ma, Jin and Yan, Jingwen and Aldeen, Mohammed and Anderson, Ethan and Kavuru, Taran and Park, Jinkyung Katie and Luo, Feng and Cheng, Long}, note = {arXiv:2603.22453; Under review}, eprint = {2603.22453}, doi = {10.48550/arXiv.2603.22453}, url = {https://arxiv.org/pdf/2603.22453}, year = {2026} } - 2025Ma, J., Aldeen, M., Salas, C., Luo, F., Chowdhury, M., Pese, M., & Cheng, L. (2025). DisPatch: Disarming Adversarial Patches in Object Detection with Diffusion Models. arXiv:2509.04597; Under review. https://arxiv.org/pdf/2509.04597
@unpublished{ma2025dispatch, title = {DisPatch: Disarming Adversarial Patches in Object Detection with Diffusion Models}, author = {Ma, Jin and Aldeen, Mohammed and Salas, Christopher and Luo, Feng and Chowdhury, Mashrur and Pese, Mert and Cheng, Long}, note = {arXiv:2509.04597; Under review}, eprint = {2509.04597}, doi = {10.48550/arXiv.2509.04597}, url = {https://arxiv.org/pdf/2509.04597}, year = {2025} }We introduce DisPatch, the first diffusion-based defense framework for object detection against adversarial patch attacks.