Zijin Yang

Orcid: 0009-0008-8899-2249

According to our database1, Zijin Yang authored at least 13 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
SWIFT: Sliding Window Reconstruction for Few-Shot Training-Free Generated Video Attribution.
CoRR, March, 2026

WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models.
CoRR, January, 2026

SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks.
CoRR, January, 2026

AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models.
CoRR, April, 2025

Provably Secure Image Robust Steganography via Cross-modal Error Correction.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

CoSDA: Enhancing the Robustness of Inversion-based Generative Image Watermarking Framework.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
Provably Secure Robust Image Steganography.
IEEE Trans. Multim., 2024

Provably Secure Robust Image Steganography via Cross-Modal Error Correction.
CoRR, 2024

SemGIR: Semantic-Guided Image Regeneration Based Method for AI-generated Image Detection and Attribution.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

A Geometric Distortion Immunized Deep Watermarking Framework with Robustness Generalizability.
Proceedings of the Computer Vision - ECCV 2024, 2024

Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2021
Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data.
Proceedings of the Bildverarbeitung für die Medizin 2021, 2021


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