The Limitations of Deep Learning in Adversarial Settings论文笔记
0.摘要 基于对DNNs输入输出映射的精确理解，提出了JSMA算法构建对抗样本，在实验中取得优异效果，最后描述了通过定义一个良性输入和目标分类之间的距离的预测度量来防范对抗样本的初步工作。 1.介绍 1.深度学习应用广泛 2.举例：实际生活中对抗样本的危害 3.举例：对抗样本如何发挥作用 4.阐明本文工作 5.数学形式定义需解决问题 JSMA为目标攻击，输入X加上扰动后，神经网络将其归类为Y*，我
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相关文章
- 1. [paper]The Limitations of Deep Learning in Adversarial Settings(JSMA）
- 2. The Limitations of Deep Learning in Adversarial Settings
- 3. 【论文回顾】The Limitations of Deep Learning in Adversarial Settings
- 4. 关于The Limitations of Deep Learning in Adversarial Settings的理解
- 5. 对The Limitations of Deep Learning in Adversarial Settings理解
- 6. Deep Learning 论文笔记 (3): Deep Learning Face Attributes in the Wild
- 7. [论文解读]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 8. 论文阅读笔记《Deep Meta-Learning: Learning to Learn in the Concept Space》
- 9. 【论文笔记】Deep Learning on Graphs: A Survey
- 10. Machine Learning & Deep Learning 论文阅读笔记