9 papers
Rethinking LLM Ensembling from the Perspective of Mixture Models
Jiale Fu, Yuchu Jiang, Peijun Wu +3
Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multi…
dCache: Accelerating Diffusion-Based LLMs via Dual Adaptive Caching
Yuchu Jiang, Yue Cai, Xiangzhong Luo +4
Diffusion-based large language models (dLLMs), despite their promising performance, still suffer from inferior inference efficiency. This is because dLLMs rely on bidirectional att…
Loupe: A Generalizable and Adaptive Framework for Image Forgery Detection
Yuchu Jiang, Jiaming Chu, Jian Zhao +5
The proliferation of generative models has raised serious concerns about visual content forgery. Existing deepfake detection methods primarily target either image-level classificat…
ERF-BA-TFD+: A Multimodal Model for Audio-Visual Deepfake Detection
Xin Zhang, Jiaming Chu, Jian Zhao +5
Deepfake detection is a critical task in identifying manipulated multimedia content. In real-world scenarios, deepfake content can manifest across multiple modalities, including au…
Never compromise with vulnerabilities: a comprehensive survey on AI governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan +64
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, t…
Safe Semantics, Unsafe Interpretations: Tackling Implicit Reasoning Safety in Large Vision-Language Models
Wei Cai, Jian Zhao, Yuchu Jiang +2
Large Vision-Language Models face growing safety challenges with multimodal inputs. This paper introduces the concept of Implicit Reasoning Safety, a vulnerability in LVLMs. Benign…