149 citations · 184 across the 13 of their papers we have counts for
13 papers
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Haibo Jin, Ruoxi Chen, Peiyan Zhang +2
The discovery of "jailbreaks" to bypass safety filters of Large Language Models (LLMs) and harmful responses have encouraged the community to implement safety measures. One major s…
EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models
Ruoxi Chen, Haibo Jin, Yixin Liu +3
Text-to-image diffusion models have emerged as an evolutionary for producing creative content in image synthesis. Based on the impressive generation abilities of these models, inst…
Unsupervised Domain Adaptation for Anatomical Landmark Detection
Haibo Jin, Haoxuan Che, Hao Chen
Recently, anatomical landmark detection has achieved great progresses on single-domain data, which usually assumes training and test sets are from the same domain. However, such an…
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
Haibo Jin, Haoxuan Che, Yi Lin +1
Automatic medical report generation (MRG) is of great research value as it has the potential to relieve radiologists from the heavy burden of report writing. Despite recent advance…
Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains
Haoxuan Che, Yuhan Cheng, Haibo Jin +1
Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide. Early and accurate grading of its severity is crucial for disease managem…
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space
Haibin Zheng, Jinyin Chen, Haibo Jin
Deep neural networks (DNNs) have demonstrated their outperformance in various software systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, i…
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking
Ruoxi Chen, Haibo Jin, Jinyin Chen +1
Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to character…
Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement
Haoxuan Che, Haibo Jin, Hao Chen
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of permanent blindness worldwide. Designing an automatic grading system with good generalization abili…
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Haibo Jin, Ruoxi Chen, Haibin Zheng +4
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequen…
Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence
Ruoxi Chen, Haibo Jin, Haibin Zheng +2
The vulnerabilities of deep learning models towards adversarial attacks have attracted increasing attention, especially when models are deployed in security-critical domains. Numer…
When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model
Haibo Jin, Jinpeng Li, Shengcai Liao +1
In recent years, significant progress has been made in the research of facial landmark detection. However, few prior works have thoroughly discussed about models for practical appl…
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
Haibo Jin, Shengcai Liao, Ling Shao
Recently, heatmap regression models have become popular due to their superior performance in locating facial landmarks. However, three major problems still exist among these models…
Deep Person Re-Identification with Improved Embedding and Efficient Training
Haibo Jin, Xiaobo Wang, Shengcai Liao +1
Person re-identification task has been greatly boosted by deep convolutional neural networks (CNNs) in recent years. The core of which is to enlarge the inter-class distinction as…