Adversarial Machine Learning and Beyond - Philipp Benz and Chaoning Zhang

This talk will introduce Adversarial Machine Learning in general -A branch of ML research focused on the development of secure and robust models through a process of attempting to deceive models using malicious or false inputs.

The talk is partially based on several recent accepted papers by the authors:

CD-UAP: Class Discriminative Universal Adversarial Perturbation - AAAI 2020
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations - CVPR 2020
Double Targeted Universal Adversarial Perturbations - ACCV 2020
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging - NeurIPS 2020

Lecture abstract:

Despite their great success and popularity, deep neural networks are widely known to be 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. More strikingly, a single perturbation has been found to fool a model for most images. In this talk, we will give an introduction to adversarial attack and defense as well as the most recent progress on universal adversarial perturbations. Beyond the adversarial use, we also show that small imperceptible perturbations can be utilized to hide useful information for steganography, watermarking, and light field messaging.

Presenters BIO:

Chaoning Zhang and Philipp Benz are 3rd and 4th 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.

Links:

Slides: https://phibenz.github.io/talk/2d3d.ai/2d3dai_adversarial_machine_learning_and_beyond.pdf
Philipp Benz: https://phibenz.github.io
Chaoning Zhang: https://chaoningzhang.github.io
RCV-Lab: https://rcv.kaist.ac.kr

This is a technical talk, prior knowledge of deep learning is advised.

** ** 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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