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QEBA: Query-Efficient Boundary-Based Blackbox Attack
Machine learning (ML), especially deep neural networks (DNNs) have been ...
05/28/2020 ∙ by Huichen Li, et al. ∙
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RAB: Provable Robustness Against Backdoor Attacks
Recent studies have shown that deep neural networks (DNNs) are vulnerabl...
03/19/2020 ∙ by Maurice Weber, et al. ∙
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Provable Robust Learning Based on Transformation-Specific Smoothing
As machine learning systems become pervasive, safeguarding their securit...
02/27/2020 ∙ by Linyi Li, et al. ∙
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Detecting AI Trojans Using Meta Neural Analysis
Machine learning models, especially neural networks (NNs), have achieved...
10/08/2019 ∙ by Xiaojun Xu, et al. ∙
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A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks
Malicious calls, i.e., telephony spams and scams, have been a long-stand...
04/07/2018 ∙ by Huichen Li, et al. ∙
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A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models...
11/30/2017 ∙ by Rui Luo, et al. ∙
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SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Synthesizing SQL queries from natural language is a long-standing open p...
11/13/2017 ∙ by Xiaojun Xu, et al. ∙
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Can you fool AI with adversarial examples on a visual Turing test?
Deep learning has achieved impressive results in many areas of Computer ...
09/25/2017 ∙ by Xiaojun Xu, et al. ∙
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Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
The problem of cross-platform binary code similarity detection aims at d...
08/22/2017 ∙ by Xiaojun Xu, et al. ∙
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