5 citations · 9 across the 11 of their papers we have counts for
11 papers
Privacy-Preserving Robustness Verification for Neural Networks
Nianyun Song, Xiaokun Luan, Yu Guo +3
Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both are increasingly restricted by…
Stabilizing Multi-Attack Adversarial Training via Bandit Optimization
Rui Wang, Zeming Wei, Xiyue Zhang +1
Deep Neural Networks (DNNs) remain vulnerable to diverse adversarial perturbations, motivating multi-attack adversarial training (AT) for improved robustness. However, existing met…
Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis
Xiyue Zhang, Xiaoyong Xue, Xiaoning Du +3
Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning fro…
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Xiaokun Luan, Xiyue Zhang, Jingyi Wang +1
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and…
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Marta Kwiatkowska, Xiyue Zhang
Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis an…
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks
Zeming Wei, Xiyue Zhang, Yihao Zhang +1
Recurrent Neural Networks (RNNs) have achieved tremendous success in processing sequential data, yet understanding and analyzing their behaviours remains a significant challenge. T…
Provable Preimage Under-Approximation for Neural Networks (Full Version)
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska
Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is im…
Using Z3 for Formal Modeling and Verification of FNN Global Robustness
Yihao Zhang, Zeming Wei, Xiyue Zhang +1
While Feedforward Neural Networks (FNNs) have achieved remarkable success in various tasks, they are vulnerable to adversarial examples. Several techniques have been developed to v…
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages
Zeming Wei, Xiyue Zhang, Meng Sun
Recurrent Neural Networks (RNNs) have achieved tremendous success in sequential data processing. However, it is quite challenging to interpret and verify RNNs' behaviors directly.…
Global Robustness Verification Networks
Weidi Sun, Yuteng Lu, Xiyue Zhang +2
The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns. Existing adversarial attack generation and…
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Xiyue Zhang, Xiaofei Xie, Lei Ma +5
Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers fro…