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20222025
most citedAssessing Privacy Compliance of Android Third-Party SDKs

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Seeing It Before It Happens: In-Generation NSFW Detection for Diffusion-Based Text-to-Image Models

Fan Yang, Yihao Huang, Jiayi Zhu +4

Diffusion-based text-to-image (T2I) models enable high-quality image generation but also pose significant risks of misuse, particularly in producing not-safe-for-work (NSFW) conten…

cs.CR2025

Beyond the Scope: Security Testing of Permission Management in Team Workspace

Liuhuo Wan, Chuan Yan, Mark Huasong Meng +4

Nowadays team workspaces are widely adopted for multi-user collaboration and digital resource management. To further broaden real-world applications, mainstream team workspaces pla…

cs.CR2024★ 2 cited

Assessing Privacy Compliance of Android Third-Party SDKs

Mark Huasong Meng, Chuan Yan, Qing Zhang +5

Third-party Software Development Kits (SDKs) are widely adopted in Android app development, to effortlessly accelerate development pipelines and enhance app functionality. However,…

cs.LG2024

PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks

Mark Huasong Meng, Hao Guan, Liuhuo Wan +3

We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process…

cs.LG2022★ 2 cited

Supervised Robustness-preserving Data-free Neural Network Pruning

Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +1

When deploying pre-trained neural network models in real-world applications, model consumers often encounter resource-constraint platforms such as mobile and smart devices. They ty…