44 citations · 95 across the 9 of their papers we have counts for
10 papers · 1 filter
CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
Siyuan Cheng, Lingjuan Lyu, Zhenting Wang +2
With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant…
Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy
David Schneider, Sina Sajadmanesh, Vikash Sehwag +4
Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence. Prevalent methods tackling this problem use differential privac…
Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget
Vikash Sehwag, Xianghao Kong, Jingtao Li +2
As scaling laws in generative AI push performance, they also simultaneously concentrate the development of these models among actors with large computational resources. With a focu…
Evaluating and Mitigating IP Infringement in Visual Generative AI
Zhenting Wang, Chen Chen, Vikash Sehwag +2
The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered…
How to Trace Latent Generative Model Generated Images without Artificial Watermark?
Zhenting Wang, Vikash Sehwag, Chen Chen +3
Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models…
Finding needles in a haystack: A Black-Box Approach to Invisible Watermark Detection
Minzhou Pan, Zhenting Wang, Xin Dong +3
In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbit…