5 papers · 1 filter
MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
Tong Zhou, Ruyi Ding, Gaowen Liu +5
The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust pro…
MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
Ruyi Ding, Tianhong Xu, Xinyi Shen +2
The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multimodal…
MACPruning: Dynamic Operation Pruning to Mitigate Side-Channel DNN Model Extraction
Ruyi Ding, Cheng Gongye, Davis Ranney +2
As deep learning gains popularity, edge IoT devices have seen proliferating deployment of pre-trained Deep Neural Network (DNN) models. These DNNs represent valuable intellectual p…
Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environment
Ruyi Ding, Tianhong Xu, Aidong Adam Ding +1
Wide deployment of machine learning models on edge devices has rendered the model intellectual property (IP) and data privacy vulnerable. We propose GNNVault, the first secure Grap…
Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing
Ruyi Ding, Tong Zhou, Lili Su +3
Adapting pre-trained deep learning models to customized tasks has become a popular choice for developers to cope with limited computational resources and data volume. More specific…