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20242026
most citedWatermark-based Attribution of AI-Generated Content

6 citations · 18 across the 17 of their papers we have counts for

collaborators

18 papers

cs.CR2026

Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions

Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu +7

Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in…

cs.CV2026

Rare Concept Generation via Counterfactual Inference in Diffusion Models

Zhengyuan Jiang, Haipeng Liu, Meng Wang +1

Rare concept generation focuses on synthesizing customized images conditioned on text prompts that describe objects with unusual attributes. Previous works failed to align the gene…

cs.CR2026

Dynamic Malicious Skills in Agentic AI

Tianhao Chen, Zhengyuan Jiang, Yuepeng Hu +2

Skills are a key enabling component of agentic AI. While they enhance agents' capabilities, they also introduce new attack surfaces. In this work, we investigate one such attack su…

cs.CR2026

Robustness of Vision Foundation Models to Common Perturbations

Hongbin Liu, Zhengyuan Jiang, Cheng Hong +1

A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, brightness, contrast adjustments). T…

cs.CR2025

A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges

Yuwen Cui, Guangjing Wang, Khanh Vu +7

The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulner…

cs.CR2025

EditTrack: Detecting and Attributing AI-assisted Image Editing

Zhengyuan Jiang, Yuyang Zhang, Moyang Guo +1

In this work, we formulate and study the problem of image-editing detection and attribution: given a base image and a suspicious image, detection seeks to determine whether the sus…