⚙ Toolbox This repository contains Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021. If you find this repo useful, please cite: A Survey of Adversarial Learning on Graph, Arxiv'20, Link @article{chen2020survey, title={A Survey...
We test our proposed framework under three attack scenarios to ensure the robustness of our solution. As the adversary’s knowledge of a system determines the success of the executed attacks, we study four gray-box cases where the adversary has access to different percentages of the victim’s ...
First, we examined the robustness of the resulting images with respect to slight changes of the input latent variables. With randomly chosen reference points for the latent variables\({{\boldsymbol{Z}}}^{{\rm{REF}}(k)}=({Z}_{1}^{{\rm{REF}}(k)},\ldots ,{Z}_{100}^{{\rm{REF...
Human-engineering features are easier to be understood and computation efficient, but the methods based on these features are often poor in robustness and can be only adapt to simple scenario. Shenpei Chen et al. [ 16] recognize armored target based on local part and latent support vector ...
We designed multiple driving states for the adversarial vehicle, including fixed natural driving behavior from the NGSIM database and three adversarial levels (low, medium, high) using the game-theoretic planning control algorithm. Considering the complexity of black-box autonomous systems, we selected...
The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy mult
To verify the robustness of the prediction performance of DCGAN-DTA, we conducted multiple adversarial control experiments. Firstly, we evaluated the method using straw models that were trained and tested on shuffled binding affinity values. We performed three different experiments: training models using...
Adversarial control experiments To verify the robustness of the prediction performance of DCGAN-DTA, we conducted multiple adversarial con- trol experiments. Firstly, we evaluated the method using straw models that were trained and tested on shuffled binding affinity values. We performed three different...