6.1節 はじめに
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Explaining and Harnessing Adversarial Examples | Goodfellow et al | 2014b | arxiv | 1 |
| Adversarial Attacks and Defenses in Images, Graphs and Text: A Review | Xu et al | 2019b | arxiv | 2 |
6.2節 グラフへの敵対的攻撃
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective | Xu et al | 2019c | arxiv | 1 |
| Towards Evaluating the Robustness of Neural Networks | Carlini and Wagner | 2017 | arxiv | 2 |
| Adversarial Examples for Graph Data: Deep Insights into Attack and Defense | Wu et al | 2019 | リンク | 3 |
| Explaining and Harnessing Adversarial Examples | Goodfellow et al | 2014b | arxiv | 4 |
| Axiomatic Attribution for Deep Networks | Sundararajan et al | 2017 | arxiv | 5 |
| Adversarial Attacks on Neural Networks for Graph Data | Zügner et al | 2018 | arxiv | 6 |
| Adversarial Attacks on Graph Neural Networks via Meta Learning | Zügner and Günnemann | 2019 | arxiv | 7 |
| Adversarial Attack on Graph Structured Data | Dai et al | 2018 | arxiv | 8 |
| Q-learning | Watkins and Dayan | 1992 | リンク | 9 |
| Attacking Graph Convolutional Networks via Rewiring | Ma et al | 2019d | arxiv | 10 |
| Policy Gradient Methods for Reinforcement Learning with Function Approximation | Sutton et al | 2000 | リンク | 11 |
6.3節 敵対的攻撃に対する防御
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Explaining and Harnessing Adversarial Examples | Goodfellow et al | 2014b | arxiv | 1 |
| Adversarial Attack on Graph Structured Data | Dai et al | 2018 | arxiv | 2 |
| Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure | Feng et al | 2019a | arxiv | 3 |
| International encyclopedia of statistical science: Kullback-Leibler Divergence | Joyce | 2011 | リンク | 4 |
| Latent Adversarial Training of Graph Convolution Networks | Jin and Zhang | 2019 | 5 | |
| Adversarial Examples for Graph Data: Deep Insights into Attack and Defense | Wu et al | 2019 | リンク | 6 |
| Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies | Jin et al | 2020a | arxiv | 7 |
| Introduction To Data Mining | Tan et al | 2021 | amazon | 8 |
| All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs | Entezari et al | 2020 | リンク | 9 |
| Robust Graph Convolutional Networks Against Adversarial Attacks | Zhu et al | 2019a | 10 | |
| Transferring Robustness for Graph Neural Network Against Poisoning Attacks | Tang et al | 2019 | arxiv | 11 |
| Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Finn et al | 2017 | arxiv | 12 |
| Graph Structure Learning for Robust Graph Neural Networks | Jin et al | 2020b | arxiv | 13 |
6.5節 参考文献
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses | Li et al | 2020a | arxiv | 1 |
| Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies | Jin et al | 2020a | arxiv | 2 |
| Adversarial Examples: Attacks and Defenses for Deep Learning | Yuan et al | 2019b | arxiv | 3 |
| Adversarial Attacks and Defenses in Images, Graphs and Text: A Review | Xu et al | 2019b | arxiv | 4 |
| Adversarial Attacks and Defenses in Deep Learning | Ren et al | 2020 | リンク | 5 |
| Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey | Zhang et al | 2020 | arxiv | 6 |