David Wagner | DeepAI

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Trevor Darrell
202 publications
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120 publications
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87 publications
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56 publications
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42 publications
Suman Jana
35 publications
Neil Shah
31 publications
Saining Xie
28 publications
Alexander Kirillov
25 publications
Dequan Wang
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Evan Shelhamer
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∙ 04/01/2023

DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

We propose and release a new vulnerable source code dataset. We curate t...

0 Yizheng Chen, et al. ∙

∙ 02/08/2023

Continuous Learning for Android Malware Detection

Machine learning methods can detect Android malware with very high accur...

0 Yizheng Chen, et al. ∙

∙ 01/12/2022

Diffix Elm: Simple Diffix

Historically, strong data anonymization requires substantial domain expe...

0 Paul Francis, et al. ∙

∙ 12/23/2021

SLIP: Self-supervision meets Language-Image Pre-training

Recent work has shown that self-supervised pre-training leads to improve...

12 Norman Mu, et al. ∙

∙ 05/27/2021

Hopper: Modeling and Detecting Lateral Movement (Extended Report)

In successful enterprise attacks, adversaries often need to gain access ...

0 Grant Ho, et al. ∙

∙ 05/24/2021

Learning Security Classifiers with Verified Global Robustness Properties

Recent works have proposed methods to train classifiers with local robus...

5 Yizheng Chen, et al. ∙

∙ 05/18/2021

Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks

Adversarial attacks optimize against models to defeat defenses. Existing...

20 Dequan Wang, et al. ∙

∙ 03/01/2021

Model-Agnostic Defense for Lane Detection against Adversarial Attack

Susceptibility of neural networks to adversarial attack prompts serious ...

2 Henry Xu, et al. ∙

∙ 11/19/2020

Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams

Adversarial examples are a widely studied phenomenon in machine learning...

24 Chawin Sitawarin, et al. ∙

∙ 07/28/2020

A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts

We present a large-scale characterization of attacker activity across 11...

0 Neil Shah, et al. ∙

∙ 04/28/2020

Minority Reports Defense: Defending Against Adversarial Patches

Deep learning image classification is vulnerable to adversarial attack, ...

8 Michael McCoyd, et al. ∙

∙ 03/18/2020

Improving Adversarial Robustness Through Progressive Hardening

Adversarial training (AT) has become a popular choice for training robus...

1 Chawin Sitawarin, et al. ∙

∙ 03/14/2020

Minimum-Norm Adversarial Examples on KNN and KNN-Based Models

We study the robustness against adversarial examples of kNN classifiers ...

10 Chawin Sitawarin, et al. ∙

∙ 10/02/2019

Detecting and Characterizing Lateral Phishing at Scale

We present the first large-scale characterization of lateral phishing at...

0 Grant Ho, et al. ∙

∙ 07/12/2019

Stateful Detection of Black-Box Adversarial Attacks

The problem of adversarial examples, evasion attacks on machine learning...

0 Steven Chen, et al. ∙

∙ 06/23/2019

Defending Against Adversarial Examples with K-Nearest Neighbor

Robustness is an increasingly important property of machine learning mod...

1 Chawin Sitawarin, et al. ∙

∙ 03/20/2019

On the Robustness of Deep K-Nearest Neighbors

Despite a large amount of attention on adversarial examples, very few wo...

18 Chawin Sitawarin, et al. ∙

∙ 02/01/2018

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

We identify obfuscated gradients as a phenomenon that leads to a false s...

0 Anish Athalye, et al. ∙

∙ 01/05/2018

Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

We construct targeted audio adversarial examples on automatic speech rec...

0 Nicholas Carlini, et al. ∙

∙ 11/22/2017

MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples

MagNet and "Efficient Defenses..." were recently proposed as a defense t...

0 Nicholas Carlini, et al. ∙

∙ 05/20/2017

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Neural networks are known to be vulnerable to adversarial examples: inpu...

0 Nicholas Carlini, et al. ∙

∙ 08/16/2016

Towards Evaluating the Robustness of Neural Networks

Neural networks provide state-of-the-art results for most machine learni...

0 Nicholas Carlini, et al. ∙

∙ 08/06/2016

Spoofing 2D Face Detection: Machines See People Who Aren't There

Machine learning is increasingly used to make sense of the physical worl...

0 Michael McCoyd, et al. ∙

∙ 07/14/2016

Defensive Distillation is Not Robust to Adversarial Examples

We show that defensive distillation is not secure: it is no more resista...

0 Nicholas Carlini, et al. ∙