Publications — Agnibh Dasgupta

A. Tanvir, A. Dasgupta, and X. Zhong, “TIACam: Text-Anchored Invariant Feature Learning with Auto-Augmentation for Camera-Robust Zero-Watermarking,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2026, in press. (Paper: https://arxiv.org/abs/2602.18863)
A. Dasgupta and X. Zhong, “Robust Image Watermarking Based on Cross-Attention and Invariant Domain Learning,” in Proc. Int. Conf. Computational Science and Computational Intelligence (CSCI), 2023, pp. 1125–1132. (Paper: https://ieeexplore.ieee.org/document/10590390; Code: https://github.com/cent664/SSRIW)
N. Khatri, A. Dasgupta, Y. Shen, X. Zhong, and F. Y. Shih, “Perspective Transformation Layer,” in Proc. Int. Conf. Computational Science and Computational Intelligence (CSCI), 2022, pp. 1395–1401. (Paper: https://ieeexplore.ieee.org/document/10216469; Code: https://github.com/kcnishan/Perspective_Transformation_Layer)
H. Yoon, X. Zhong, A. Dasgupta, G. Nugent, and G. Trainin, “Leveraging Artificial Intelligence (AI) to Enhance Computer Science Instruction,” in Proc. IEEE Frontiers in Education Conference (FIE), Washington, DC, USA, 2024, pp. 1–5. (Paper: https://ieeexplore.ieee.org/document/10893354)

A. Dasgupta, A. Tanvir, and X. Zhong, “Watermarking Language Models through Language Models,” IEEE Transactions on Artificial Intelligence, vol. 1, no. 01, pp. 1–10, 2025. (Paper: https://ieeexplore.ieee.org/document/11146861; Code: https://github.com/cent664/LLMWM)

A. Dasgupta and X. Zhong, “Enhanced Image Watermarking Through Cross-Attention and Noise-Invariant Domain Learning,” in Imaging Science: Computer Vision, Image and Signal Processing and Pattern Recognition, 2024. (Paper: https://ieeexplore.ieee.org/abstract/document/11052895)

A. Dasgupta, “Invariant Feature Learning in AI Models: Exploring Representation Spaces for Robust Watermarking and Beyond,” Ph.D. Dissertation, Dept. of Computer Science, University of Nebraska Omaha, Omaha, NE, USA, 2026. (Paper: https://digitalcommons.unomaha.edu/compscistudent/7/)
A. Dasgupta, “Deep Q Learning Applied to Stock Trading,” M.S. Thesis, Dept. of Computer Science, Utah State University, Logan, UT, USA, 2020. (Paper: https://digitalcommons.usu.edu/etd/7983/)