Blind Attacks on Machine Learners

By Alex Beatson, Zhaoran Wang, Han Liu. 2016

6 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiaoyong Yuan
2. Qile Zhu
3. Xiaolin Li
4. Pan He
5. Ramin Tavakoli Kolagari
6. Markus Zoppelt
7. Katja Auernhammer
8. Edward Chou

Citations in the corpus

• Adversarial Examples: Attacks and Defenses for Deep Learning (2017, background): “, training data poisoning) is another interesting topic and has been studied in [8], [16], [21], [98], [112], [142].”
• Attacks on Machine Learning: Lurking Danger for Accountability (2019, methodology): “Attacks we consider applicable to systems regardless of the ML algorithm, learning type, and lifelong learning capability are, for example, poisoning attacks [8, 46] as these attacks do not focus on the model but the training data; therefore, poisoning attacks are considered independent of the ML al”