activity
20172023
most citedFedMood: Federated Learning on Mobile Health Data for Mood Detection

90 citations · 563 across the 40 of their papers we have counts for

collaborators

41 papers

cs.CR2023★ 12 cited

DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models

Yingqian Cui, Jie Ren, Han Xu +5

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images. A large community of GDMs has naturally emerged, furthe…

cs.CR2023★ 23 cited

BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT

Jiawen Shi, Yixin Liu, Pan Zhou +1

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL…

cs.LG2023

Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation

Yixin Liu, Chenrui Fan, Pan Zhou +1

While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data fo…

cs.LG2023★ 1 cited

Memory-adaptive Depth-wise Heterogeneous Federated Learning

Kai Zhang, Yutong Dai, Hongyi Wang +3

Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…

cs.CR2023★ 4 cited

Backdoor Attacks to Pre-trained Unified Foundation Models

Zenghui Yuan, Yixin Liu, Kai Zhang +2

The rise of pre-trained unified foundation models breaks down the barriers between different modalities and tasks, providing comprehensive support to users with unified architectur…

cs.CR2023★ 19 cited

SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency

Junfeng Guo, Yiming Li, Xun Chen +3

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation.…