Tianyun Yang (杨天韵)

I'm a final-year PhD student at the University of Chinese Academy of Sciences (UCAS), supervised by Juan Cao.

I am interested in mechanism interpretability and safety of AI models, covering topics such as hallucination mitigation, concept editing, model attribution, etc.

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Representative Researches

This work adopts a modular perspective to investigate the causes of hallucination in large vision-language models, analyzing how particular components contribute to this issue and proposing methods to mitigate it.

This work aims to develop a generalized model fingerprint extractor capable of Zero-Shot Model Attribution that effectively attributes unseen models without exposure during training. Central to our method is a model synthesis technique, which generates numerous synthetic models that mimic the fingerprint patterns of real-world generative models.

This work designs a robust concept erasing method based on differential pruning to eliminate harmful or copyrighted concepts from diffusion models

This work presents the first study on Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. We propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces.

This work presents the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem.

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