Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.

 
 

Outline/Structure of the Talk

We will follow the following outline for the presentation:

  • What are Adversarial attacks?
  • CIA Model of Security
  • Threat models
  • Examples and demos of Adversarial attacks
  • Proposed Defenses against adversarial attacks
  • Intuition behind Adversarial attacks
  • What’s next?

Learning Outcome

This talk is motivated by the question: Are adversarial examples simply a fun toy problem for researchers or an example of a deeper and more chronic frailty in our models? The learning outcome for attendees from this talk is to realize that Deep Learning Models are just another tool, susceptible to adversarial attacks. These can have huge implications, especially in a world with self-driving cars and other automation.

Target Audience

Deep Learning Practitioners or students interested in learning more about an up-and-coming area of research in this field.

Prerequisites for Attendees

A beginner-level understanding of how Deep Neural Networks work.

Slides


Video


schedule Submitted 4 years ago

  • Dat Tran
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    Dat Tran - Image ATM - Image Classification for Everyone

    Dat Tran
    Dat Tran
    Head of AI
    Axel Springer AI
    schedule 4 years ago
    Sold Out!
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  • Dipanjan Sarkar
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    Favio Vázquez - Complete Data Science Workflows with Open Source Tools

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  • Maryam Jahanshahi
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    Maryam Jahanshahi - Applying Dynamic Embeddings in Natural Language Processing to Analyze Text over Time

    Maryam Jahanshahi
    Maryam Jahanshahi
    Research Scientist
    TapRecruit
    schedule 4 years ago
    Sold Out!
    45 Mins
    Case Study
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    Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

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  • Saurabh Jha
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    Saurabh Jha / Rohan Shravan / Usha Rengaraju - Hands on Deep Learning for Computer Vision

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  • Tanay Pant
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    Tanay Pant - Machine data: how to handle it better?

    Tanay Pant
    Tanay Pant
    Developer Advocate
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    schedule 4 years ago
    Sold Out!
    45 Mins
    Talk
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