Adversarial Transferability and Beyond

Deep Neural Networks have achieved great success in various vision tasks in recent years. However, they remain vulnerable to adversarial examples, i.e. small human imperceptible perturbations fooling a target model. This intriguing phenomenon has inspired numerous techniques for attack and defense. In this talk, we will mainly focus on the transferability property that makes adversarial examples so dangerous as well as some of the theories to understand this intriguing phenomenon. Here, transferability refers to the property that adversarial examples generated on one model successfully transfer to another, unseen model, therefore constituting a black-box attack.

Lecture slides [Will be published here in proximity to the event date]: https://phibenz.github.io/talk/2d3d.ai/2d3dai_adversarial_transferability_and_beyond.pdf

The reconstruction focuses on the authors' papers:

1. Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs - Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (AML-CV) (Outstanding Paper) - Will be published in June

2. On Strength and Transferability of Adversarial Examples: Stronger Attack Transfers Better
   Paper: https://sites.google.com/connect.hku.hk/robustml-2021/accepted-papers/paper-099

Presenters BIO:

Chaoning Zhang and Philipp Benz are 4th and 5th year Ph.D. students at the Robotics and Computer Vision (RCV) Lab at the Korea Advanced Institute of Science and Technology (KAIST) supervised by Prof. Kweon In So. Their research interest lies in deep learning with a focus on robustness and security. Through their collaborative efforts, they published papers at top conferences like CVPR, NeurIPS, and AAAI and are always open to collaborations with other researchers.

Philipp Benz: https://phibenz.github.io

Chaoning Zhang: https://scholar.google.co.kr/citations?user=lvhxhyQAAAAJ&hl=en

RCV-Lab: https://rcv.kaist.ac.kr

A recording of Philipp and Chaoning's previous event in our community: https://www.youtube.com/watch?v=ylEE1HtGNJc

** ** Please register through the zoom link right after your RSVP. We will send the links to the zoom event via email only to those who have registered through zoom. ** **

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