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09/20/2023
It's Simplex! Disaggregating Measures to Improve Certified Robustness
Certified robustness circumvents the fragility of defences against adver...
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08/15/2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
Poisoning attacks can disproportionately influence model behaviour by ma...
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04/26/2023
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Deep learning models, even the-state-of-the-art ones, are highly vulnera...
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02/09/2023
Exploiting Certified Defences to Attack Randomised Smoothing
In guaranteeing that no adversarial examples exist within a bounded regi...
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10/12/2022
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
In response to subtle adversarial examples flipping classifications of n...
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06/21/2022
Transferable Graph Backdoor Attack
Graph Neural Networks (GNNs) have achieved tremendous success in many gr...
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01/25/2021
Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning
Contrastive learning (CL) has recently emerged as an effective approach ...
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10/13/2020
Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images
Deep neural network image classifiers are reported to be susceptible to ...
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09/21/2020
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Ensemble-based adversarial training is a principled approach to achieve ...
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07/10/2020
Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbati...
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10/03/2019
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
Deep neural network image classifiers are reported to be susceptible to ...
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02/25/2019
Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking
Recent studies have demonstrated that reinforcement learning (RL) agents...
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08/17/2018
