collaborators

14 papers

cs.CV2026

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors

Xia Du, Zhuosen Bao, Zheng Lin +6

Recent diffusion models have achieved remarkable realism in facial image synthesis, posing growing challenges to artificial intelligence-generated content (AIGC) forensic detectors…

cs.NI2026

FluxShard: Motion-Aware Feature Cache Reuse for Collaborative Video Analytics in Mobile Edge Computing

Xiuxian Guan, Zongyuan Zhang, Zheng Lin +8

Caching and reusing intermediate features across consecutive frames is a common technique to reduce redundant computation and transmission for edge-cloud video analytics in mobile…

cs.CV2026

Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification

Weiwei Zhuang, Wangze Xie, Qi Zhang +9

Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification. Most existing methods rely on direct pixel-wise per…

eess.SY2026

Dual-Envelope Constrained Nonlinear MPC for Distributed Drive Electric Vehicles Drifting Under Bounded Steering and Direct Yaw-Moment Control

Yurun Gan, Ziyu Song, Jing Yang +8

Distributed drive electric vehicles offer superior yaw moment control for autonomous drifting in extreme maneuvers. Conventional drift analysis constructs stability boundaries from…

cs.LG2026

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

Zehang Lin, Miao Yang, Haihan Zhu +9

The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…

cs.LG2026

GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems

Zheng Lin, Ons Aouedi, Zihan Fang +4

The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (P…