33 citations · 162 across the 27 of their papers we have counts for
27 papers
Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking
Haoran Ou, Gelei Deng, Xingshuo Han +4
The increasing multimodal disinformation, where deceptive claims are reinforced through coordinated text and visual content, poses significant challenges to automated fact-checking…
Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
Guanlin Li, Kangjie Chen, Shangwei Guo +6
Large language models (LLMs) have emerged as powerful tools for addressing a wide range of general inquiries and tasks. Despite this, fine-tuning aligned LLMs on smaller, domain-sp…
Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability
Hao Wang, Shangwei Guo, Jialing He +3
Pre-trained models (PTMs) are widely adopted across various downstream tasks in the machine learning supply chain. Adopting untrustworthy PTMs introduces significant security risks…
Rethinking Adversarial Training with Neural Tangent Kernel
Guanlin Li, Han Qiu, Shangwei Guo +2
Adversarial training (AT) is an important and attractive topic in deep learning security, exhibiting mysteries and odd properties. Recent studies of neural network training dynamic…
Warfare:Breaking the Watermark Protection of AI-Generated Content
Guanlin Li, Yifei Chen, Jie Zhang +5
AI-Generated Content (AIGC) is rapidly expanding, with services using advanced generative models to create realistic images and fluent text. Regulating such content is crucial to p…
Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding
Hangcheng Liu, Tao Xiang, Shangwei Guo +3
Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In t…
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator
Xiaobei Yan, Xiaoxuan Lou, Guowen Xu +4
DNN accelerators have been widely deployed in many scenarios to speed up the inference process and reduce the energy consumption. One big concern about the usage of the accelerator…
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network
Ziheng Huang, Boheng Li, Yan Cai +5
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis. However, well-trained GANs are under the threat of…
Text Classification via Large Language Models
Xiaofei Sun, Xiaoya Li, Jiwei Li +4
Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classi…
Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding
Han Li, Hangcheng Liu, Shangwei Guo +4
Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing w…
Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption
Guowen Xu, Guanlin Li, Shangwei Guo +2
Decentralized deep learning plays a key role in collaborative model training due to its attractive properties, including tolerating high network latency and less prone to single-po…
CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers
Jun Li, Shangwei Guo, Shaokun Han
Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point clo…
Threats to Pre-trained Language Models: Survey and Taxonomy
Shangwei Guo, Chunlong Xie, Jiwei Li +2
Pre-trained language models (PTLMs) have achieved great success and remarkable performance over a wide range of natural language processing (NLP) tasks. However, there are also gro…
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey
Shangwei Guo, Xu Zhang, Fei Yang +4
With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example…
Protecting Your NLG Models with Semantic and Robust Watermarks
Tao Xiang, Chunlong Xie, Shangwei Guo +2
Natural language generation (NLG) applications have gained great popularity due to the powerful deep learning techniques and large training corpus. The deployed NLG models may be s…
CT-block: a novel local and global features extractor for point cloud
Shangwei Guo, Jun Li, Zhengchao Lai +2
Deep learning on the point cloud is increasingly developing. Grouping the point with its neighbors and conducting convolution-like operation on them can learn the local feature of…
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Leilei Gan, Jiwei Li, Tianwei Zhang +6
Backdoor attacks pose a new threat to NLP models. A standard strategy to construct poisoned data in backdoor attacks is to insert triggers (e.g., rare words) into selected sentence…
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Kangjie Chen, Yuxian Meng, Xiaofei Sun +4
Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language mod…
PEEL: A Provable Removal Attack on Deep Hiding
Tao Xiang, Hangcheng Liu, Shangwei Guo +1
Deep hiding, embedding images into another using deep neural networks, has shown its great power in increasing the message capacity and robustness. In this paper, we conduct an in-…
Fingerprinting Image-to-Image Generative Adversarial Networks
Guanlin Li, Guowen Xu, Han Qiu +5
Generative Adversarial Networks (GANs) have been widely used in various application scenarios. Since the production of a commercial GAN requires substantial computational and human…
Ownership Verification of DNN Architectures via Hardware Cache Side Channels
Xiaoxuan Lou, Shangwei Guo, Jiwei Li +1
Deep Neural Networks (DNN) are gaining higher commercial values in computer vision applications, e.g., image classification, video analytics, etc. This calls for urgent demands of…
Local Black-box Adversarial Attacks: A Query Efficient Approach
Tao Xiang, Hangcheng Liu, Shangwei Guo +2
Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting wi…
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
Han Qiu, Yi Zeng, Shangwei Guo +3
Public resources and services (e.g., datasets, training platforms, pre-trained models) have been widely adopted to ease the development of Deep Learning-based applications. However…
Privacy-preserving Collaborative Learning with Automatic Transformation Search
Wei Gao, Shangwei Guo, Tianwei Zhang +3
Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their trainin…