Xinyi Shang
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The paper introduces LLMSurgeon, a framework that estimates the domain-level data mixture of a Large Language Model (LLM) using only generated text, thereby providing a post-hoc method to audit the model's 'digital DNA'.
The paper introduces OpAI-Bench, a novel benchmark designed to study how AI authorship signals evolve and accumulate during the progressive co-editing process between humans and AI.
This paper proposes a domain-generalized training framework for pixel-level image tampering detection in modern vision-language models, improving robustness and out-of-distribution performance.
This paper introduces a new approach for high-dimensional one-step generation using a Three-Body Scattering Model (TBSM) with a proper distributional energy.
Papers
Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs
Yi Tang, Xinyi Shang, Jiacheng Cui, Sondos Mahmoud Bsharat +11 more
This paper proposes a domain-generalized training framework for pixel-level image tampering detection in modern vision-language models, improving robustness and out-of-distribution performance.