|Organizers||Talal Albacha , Jean-Noël Colin|
|Participants||Sebastien Deleersnyder Sebastien Deleersnyder|
Deep Learning and Machine Learning become vital part of critical systems like self-driving cars, advanced authentication and automated detection of lesions/tumors. However, research shows that such technologies have inherent risks originated from the process of how the models are being learnt or used. In this session we will learn about OWASP project (Top 5 Machine Learning Risks) which tries to identify and document these risks in general, and then we will discuss one case study about specific risk and how to address it.
- Top 5 Machine Learning Risks Project Introduction
- project team
- update about current state of document
- Developing attacks against machine learning models.
- Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (Chen et al. 2017)
Define risk rating approach for this type of attacks and suggest defence techniques
- Application security professionals
- AI professionals
- project documentation file
- paper file https://arxiv.org/abs/1712.05526
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