Keynotes

To Defend Against Adversarial Examples We Need to Understand Human Vision
Wieland Brendel, Tübingen AI Center, Germany

Abstract — Adversarial examples are among the most striking failure cases of modern deep neural networks and defending against them has proven notoriously difficult. In this talk I discuss the connection between adversarial examples and a much larger class of failure cases that arise from machines exploiting shortcuts in the given task formulation. Shortcut learning is astonishingly difficult (or even impossible) to detect in the typical i.i.d. formulation of machine learning tasks. However, testing machines in more challenging out-of-distribution (o.o.d.) scenarios — such as adversarial examples — often highlight a clear difference to the decision strategies humans employ. Closing this gap is unlikely to arise from a single change in the architecture or the training procedure, but will likely require a combined effort that aligns the tasks, the data, the learning strategies and the model architectures closer with the human visual system and the evolutionary pressures that have pushed humans to develop robust visual representations and decision strategies.

Biography — Wieland Brendel is a principal investigator at the Tübingen AI Center (Germany). His research focuses on the robustness and interpretability of machine vision models and draws inspiration from human perception. He received his M.S. in Physics from the University of Regensburg in 2009 and his Ph.D. in Computational Neuroscience at the Ecole Normale Superieure Paris in 2014. After a four-year post-doctoral fellowship in Machine Learning at the University of Tübingen he joined the Tübingen AI Center as a PI in 2018.

Detecting Deep-Fake Videos from Appearance and Behavior
Hany Farid, University of California, Berkeley

Abstract — Synthetically-generated audios and videos -- so-called deep fakes -- continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel dis-information campaigns, and create non-consensual pornography. I will describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. I will show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.

Biography — Hany Farid is a Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information. His research focuses on digital forensics, image analysis, and human perception. He received his undergraduate degree in Computer Science and Applied Mathematics from the University of Rochester in 1989, his M.S. in Computer Science from SUNY Albany, and his Ph.D. in Computer Science from the University of Pennsylvania in 1997. Following a two-year post-doctoral fellowship in Brain and Cognitive Sciences at MIT, he joined the faculty at Dartmouth College in 1999 where he remained until 2019. He is the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and is a Fellow of the National Academy of Inventors.

Call for Papers

Important Dates

• Paper submission deadline (extended): January 13 20, 2020, 11:59 PM (AoE, UTC-12)
• Acceptance notification: February 24, 2020
• Camera-ready due: March 9, 2020
• Workshop: May 21, 2020

Overview

Deep learning and security have made remarkable progress in the last years. On the one hand, neural networks have been recognized as a promising tool for security in academia and industry. On the other hand, the security of deep learning has gained focus in research, the robustness of neural networks has recently been called into question.

This workshop strives for bringing these two complementary views together by (a) exploring deep learning as a tool for security as well as (b) investigating the security of deep learning.

Topics of Interest

DLS seeks contributions on all aspects of deep learning and security. Topics of interest include (but are not limited to):

Deep Learning

• Deep learning for program embedding and similarity
• Deep program learning
• Modern deep NLP
• Recurrent network architectures
• Neural networks for graphs
• Neural Turing machines
• Semantic knowledge-bases
• Generative adversarial networks
• Relational modeling and prediction
• Deep reinforcement learning
• Attacks against deep learning
• Resilient and explainable deep learning

Computer Security

• Computer forensics
• Spam detection
• Phishing detection and prevention
• Botnet detection
• Intrusion detection and response
• Malware identification, analysis, and similarity
• Data anonymization/ de-anonymization
• Security in social networks
• Vulnerability discovery

Submission Guidelines

You are invited to submit papers of up to six pages, plus one page for references. To be considered, papers must be received by the submission deadline (see Important Dates). Submissions must be original work and may not be under submission to another venue at the time of review.

Papers must be formatted for US letter (not A4) size paper. The text must be formatted in a two-column layout, with columns no more than 9.5 in. tall and 3.5 in. wide. The text must be in Times font, 10-point or larger, with 11-point or larger line spacing. Authors are strongly recommended to use the latest IEEE conference proceedings templates. Failure to adhere to the page limit and formatting requirements are grounds for rejection without review. Submissions must be in English and properly anonymized.

Presentation Form

All accepted submissions will be presented at the workshop and included in the IEEE workshop proceedings. Due to time constraints, accepted papers will be selected for presentation as either talk or poster based on their review score and novelty. Nonetheless, all accepted papers should be considered as having equal importance.

One author of each accepted paper is required to attend the workshop and present the paper for it to be included in the proceedings.

Submission Site

Submission link: https://easychair.org/conferences/?conf=dls2020.

All questions about submissions should be emailed to dlsec2020@gmail.com.

Sponsor