Securing Machine Learning Models

ML models are popping up everywhere around us, be it e-commerce, networks or healthcare. We went through a journey of running these models on a local machine to industrializing these models and scaling them to serve millions of users. However, very few people actually realize how easy / hard these models are to hack & replicate using various black box & white box methodologies.

We walk you through important security aspects one has to keep in mind while deploying machine learning models on cloud, edge or on-premise. We will also showcase counter measures to defend these attacks as well.

We will take the standard security expert's approach of:
1. Awareness
2. Applicability
3. Countermeasures

The talk will majorly focus attacks like:
1. Model extraction - How can an adversary replicate your model?
2. Model evasion / adversarial attacks - How can an adversary corrupt your model?
3. Model watermarking - How can one prove ownership of a model?

Transcript

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