Selected Journal Papers

1. 2026

   Ma, 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. Accepted. https://arxiv.org/pdf/2508.02530

   PDF BIB Abstract

   @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 = {Accepted},
     year = {2026},
     url = {https://arxiv.org/pdf/2508.02530}
   }
   

   We investigate the impact of asphalt art on the reliability of surveillance perception systems.

2. 2023

   Liu, 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

   PDF DOI BIB

   @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}
   }
   

Selected Conference Papers

1. 2025

   Wang, 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

   PDF DOI BIB

   @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}
   }
   

2. 2025

   Han, 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.

   DOI BIB Abstract

   @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)},
     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.

3. 2025

   Yan, 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

   PDF BIB Abstract

   @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.

4. 2025

   Ma, 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

   PDF DOI BIB Abstract

   @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},
     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.

5. 2020

   Ma, 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

   PDF BIB Abstract

   @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},
     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.

Preprints

1. 2026

   Ma, 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. https://arxiv.org/pdf/2603.22453

   PDF arXiv BIB

   @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},
     eprint = {2603.22453},
     url = {https://arxiv.org/pdf/2603.22453},
     year = {2026}
   }
   

2. 2025

   Ma, 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. https://arxiv.org/pdf/2509.04597

   PDF arXiv BIB Abstract

   @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},
     eprint = {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.