5 citations · 12 across the 8 of their papers we have counts for
8 papers
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
Qingyuan Liu, Pengyuan Shi, Yun-Yun Tsai +2
The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat De…
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
Chenshuang Zhang, Fei Pan, Junmo Kim +2
We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of evaluation over s…
SelfIE: Self-Interpretation of Large Language Model Embeddings
Haozhe Chen, Carl Vondrick, Chengzhi Mao
How do large language models (LLMs) obtain their answers? The ability to explain and control an LLM's reasoning process is key for reliability, transparency, and future model devel…
Raidar: geneRative AI Detection viA Rewriting
Chengzhi Mao, Carl Vondrick, Hao Wang +1
We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often pe…
Robustifying Language Models with Test-Time Adaptation
Noah Thomas McDermott, Junfeng Yang, Chengzhi Mao
Large-scale language models achieved state-of-the-art performance over a number of language tasks. However, they fail on adversarial language examples, which are sentences optimize…
Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen, Junfeng Yang, Carl Vondrick +1
Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the u…