most citedOn the Information-Theoretic Fragility of Robust Watermarking under Diffusion Editing

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR20251 cited

On the Information-Theoretic Fragility of Robust Watermarking under Diffusion Editing

Yunyi Ni, Ziyu Yang, Ze Niu +2

Robust invisible watermarking embeds hidden information in images such that the watermark can survive various manipulations. However, the emergence of powerful diffusion-based imag…

cs.CV2025

Diffusion-Based Image Editing for Breaking Robust Watermarks

Yunyi Ni, Finn Carter, Ze Niu +2

Robust invisible watermarking aims to embed hidden information into images such that the watermark can survive various image manipulations. However, the rise of powerful diffusion-…

cs.LG2025

Neighborhood Sampling Does Not Learn the Same Graph Neural Network

Zehao Niu, Mihai Anitescu, Jie Chen

Neighborhood sampling is an important ingredient in the training of large-scale graph neural networks. It suppresses the exponential growth of the neighborhood size across network…

cs.CV2025

FADE: Adversarial Concept Erasure in Flow Models

Zixuan Fu, Yan Ren, Finn Carter +5

Diffusion models have demonstrated remarkable image generation capabilities, but also pose risks in privacy and fairness by memorizing sensitive concepts or perpetuating biases. We…

cs.NI2025

Distributionally Robust Wireless Semantic Communication with Large AI Models

Long Tan Le, Senura Hansaja Wanasekara, Zerun Niu +7

Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches…