【生成对抗样本】Simple Black-Box Adversarial Attacks on Deep Neural Networks
对深度神经网络的简单黑盒对抗性攻击 作者：Narodytska N，Kasiviswanathan S 2017年发表在IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 主要内容 深度神经网络容易受到精心设计的对抗性干扰，这会导致输入的错误分类。在本文中，我们专注于深度卷积神经
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相关文章
- 1. Adversarial Examples 对抗样本
- 2. Adversarial Sample misleading the Model(生成对抗样本迷惑模型)
- 3. 关于EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
- 4. 读书笔记17：Adversarial Attacks on Neural Networks for Graph Data
- 5. 对抗样本论文学习：Deep Neural Networks are Easily Fooled
- 6. Generating Adversarial Examples with Adversarial Networks 采用GAN的方法来生成对抗样本
- 7. ECCV 2020 的对抗相关论文（对抗生成、对抗攻击）
- 8. 对抗样本与生成式对抗网络
- 9. 对抗样本(论文解读四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 10. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks