7 citations · 11 across the 12 of their papers we have counts for
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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…
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…
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…
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…