333 citations · 649 across the 28 of their papers we have counts for
5 papers · 1 filter
TextBugger: Generating Adversarial Text Against Real-world Applications
Jinfeng Li, Shouling Ji, Tianyu Du +2
Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Des…
Model-Reuse Attacks on Deep Learning Systems
Yujie Ji, Xinyang Zhang, Shouling Ji +2
Many of today's machine learning (ML) systems are built by reusing an array of, often pre-trained, primitive models, each fulfilling distinct functionality (e.g., feature extractio…
Interpretable Deep Learning under Fire
Xinyang Zhang, Ningfei Wang, Hua Shen +3
Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help u…
EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report)
Yujie Ji, Xinyang Zhang, Ting Wang
Deep neural networks (DNNs) are inherently vulnerable to adversarial inputs: such maliciously crafted samples trigger DNNs to misbehave, leading to detrimental consequences for DNN…
Differentially Private Releasing via Deep Generative Model (Technical Report)
Xinyang Zhang, Shouling Ji, Ting Wang
Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community. Due to rich semantics of the da…