FAKEPCD: Fake Point Cloud Detection via Source Attribution

no code implementations • 18 Dec 2023 • Yiting Qu, Zhikun Zhang, Yun Shen, Michael Backes, Yang Zhang

Take the open-world attribution as an example, FAKEPCD attributes point clouds to known sources with an accuracy of 0. 82-0. 98 and to unknown sources with an accuracy of 0. 73-1. 00.

Attribute Cloud Detection

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

1 code implementation • 23 May 2023 • Yiting Qu, Xinyue Shen, Xinlei He, Michael Backes, Savvas Zannettou, Yang Zhang

Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world.

Prompt Stealing Attacks Against Text-to-Image Generation Models

1 code implementation • 20 Feb 2023 • Xinyue Shen, Yiting Qu, Michael Backes, Yang Zhang

In this paper, we perform the first study on understanding the threat of a novel attack, namely prompt stealing attack, which aims to steal prompts from generated images by text-to-image generation models.

Text-to-Image Generation

On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

2 code implementations • 13 Dec 2022 • Yiting Qu, Xinlei He, Shannon Pierson, Michael Backes, Yang Zhang, Savvas Zannettou

The dissemination of hateful memes online has adverse effects on social media platforms and the real world.

Contrastive Learning