Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices
Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment. However, on-device models embedded in mobile applications can be unpacked and exposed to adversaries, enabling cross-node attacks in centrally distributed ecosystems, where adversarial examples crafted on one compromised device transfer effectively to other nodes sharing the same models. To address this threat, we propose VeilNN, a defense framework that reduces adversarial transferability across on-device models via model obfuscation. The core idea of VeilNN is to introduce controlled model diversity while preservi...
| Authors | Yuyang Zhang, Kangjie Chen, Yihui Jin et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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