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1
Introduction
2
Who am I
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I know were okay
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Adding noise
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What happens
6
Image classifiers
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Terminology
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Outline
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Timeline
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Types of Attacks
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Blind Spots
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Bugs
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Examples
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Alchemy
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Generating adversarial examples
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What can we do
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Training life cycle
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Visual understanding
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Reservoir sampling
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Notable research
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Demo
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TF Classification
23
References
24
Resources
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Takeaway
26
Interview
Description:
Explore the current landscape of adversarial machine learning in this 22-minute conference talk from BSidesLV 2018. Delve into topics such as adding noise to image classifiers, various types of attacks, blind spots, and bugs in machine learning systems. Learn about generating adversarial examples, strategies for mitigation, and the importance of visual understanding in AI. Gain insights from notable research, witness a TensorFlow classification demo, and discover valuable resources for further study. Understand key takeaways and implications for the field of machine learning security.

The Current State of Adversarial Machine Learning

BSidesLV
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