7 citations · 12 across the 26 of their papers we have counts for
15 papers · 1 filter
Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models
Hao Fang, Jinyu Li, Jiawei Kong +4
While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are still underexplored. In this pape…
Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization
Hao Fang, Wenbo Yu, Bin Chen +4
Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning, allowing multiple clients to collaboratively train a global model…
Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation
Xiaochen Yang, Hao Fang, Jiawei Kong +3
Although large vision-language models (LVLMs) have demonstrated remarkable capabilities, they are prone to hallucinations in multi-image tasks. We attribute this issue to limitatio…
Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization
Yixiang Qiu, Yanhan Liu, Hongyao Yu +4
The growing complexity of Deep Neural Networks (DNNs) has led to the adoption of Split Inference (SI), a collaborative paradigm that partitions computation between edge devices and…
ICAS: Detecting Training Data from Autoregressive Image Generative Models
Hongyao Yu, Yixiang Qiu, Yiheng Yang +6
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. Howeve…
GaussTrap: Stealthy Backdoor Attacks on 3D Gaussian Splatting for Targeted Scene Misperception
Jiaxin Hong, Sixu Chen, Shuoyang Sun +7
3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for real-time scene representation and novel view synthesis, gaining traction in safety-critical applicatio…
Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP
Jiawei Kong, Hao Fang, Sihang Guo +5
While pre-trained Vision-Language Models (VLMs) such as CLIP exhibit impressive representational capabilities for multimodal data, recent studies have revealed their vulnerability…
Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models
Hao Fang, Xiaohang Sui, Hongyao Yu +5
Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational resources and large-scale datasets…
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information
Xinhao Zhong, Bin Chen, Hao Fang +3
Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that…
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
Yixiang Qiu, Hongyao Yu, Hao Fang +6
Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the priva…
A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks
Yixiang Qiu, Hao Fang, Hongyao Yu +3
Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information, raising extensive concerns about the security…
CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
Hao Fang, Jiawei Kong, Bin Chen +3
Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios. Recent studies have introduced \textit{singl…
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation
Xinhao Zhong, Hao Fang, Bin Chen +4
Dataset distillation is an emerging dataset reduction method, which condenses large-scale datasets while maintaining task accuracy. Current parameterization methods achieve enhance…
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
Hao Fang, Jiawei Kong, Wenbo Yu +5
Vision-Language Pre-training (VLP) models have exhibited unprecedented capability in many applications by taking full advantage of the multimodal alignment. However, previous studi…
Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
Hao Fang, Yixiang Qiu, Hongyao Yu +7
Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inversion (MI) attacks, which disclo…