Feature Squeezing- Invited Talk at GMU

On April 23 2019, I gave an invited talk at the ARO Invitational Workshop on Foundations of Autonomous Adaptive Cyber Systems

TalkSlide

Feature Squeezing- Invited Webinar Talk at I3P

On December 21 @ 12noon, I gave a distinguished webinar talk in the Fall 2018 webinar series of the Institute for Information Infrastructure Protection (I3P) (@ the George Washington University and SRI International).

TalkSlide

Webinar Recording @ URL

Feature Squeezing- Detecting Adversarial Examples in Deep Neural Networks

Paper Arxiv

GitHub: FeatureSqueezing

TalkSlide

Abstract

Although deep neural networks (DNNs) have achieved great success in many computer vision tasks, recent studies have shown they are vulnerable to adversarial examples. Such examples, typically generated by adding small but purposeful distortions, can frequently fool DNN models. Previous studies to defend against adversarial examples mostly focused on refining the DNN models. They have either shown limited success or suffer from the expensive computation. We propose a new strategy, \emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model’s prediction on the original input with that on the squeezed input, feature squeezing detects adversarial examples with high accuracy and few false positives. This paper explores two instances of feature squeezing: reducing the color bit depth of each pixel and smoothing using a spatial filter. These strategies are straightforward, inexpensive, and complementary to defensive methods that operate on the underlying model, such as adversarial training.

Citations

@inproceedings{Xu0Q18,
  author    = {Weilin Xu and
               David Evans and
               Yanjun Qi},
  title     = {Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks},
  booktitle = {25th Annual Network and Distributed System Security Symposium, {NDSS}
               2018, San Diego, California, USA, February 18-21, 2018},
  year      = {2018},
  crossref  = {DBLP:conf/ndss/2018},
  url       = {http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018\_03A-4\_Xu\_paper.pdf},
  timestamp = {Thu, 09 Aug 2018 10:57:16 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ndss/Xu0Q18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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EvadeML-Zoo Our Benchmarking and Visualization AE Tool is released

We are releasing EvadeML-Zoo: A Benchmarking and Visualization Tool for Adversarial Examples (with 8 pretrained deep models+ 9 state-of-art attacks).

Tool Github URL

About

We have designed and implemented EvadeML-Zoo, a benchmarking and visualization tool for research on adversarial machine learning. The goal of EvadeML-Zoo is to ease the experimental setup and help researchers evaluate and verify their results.

EvadeML-Zoo has a modular architecture and is designed to make it easy to add new datasets, pre-trained target models, attack or defense algorithms. The code is open source under the MIT license.

We have integrated three popular datasets: MNIST, CIFAR-10 and ImageNet- ILSVRC with a simple and unified interface. We offer several representative pre-trained models with state-of-the-art accuracy for each dataset including two pre-trained models for ImageNet-ILSVRC: the heavy Inception-v3 and and the lightweight MobileNet. We use Keras to access the pre-trained models because it provides a simplified interface and it is compatible with TensorFlow, which is a flexible tool for implementing attack and defense techniques.

We have integrated several existing attack algorithms as baseline for the upcoming new methods, including FGSM, BIM, JSMA, Deepfool, Universal Adversarial Perturbations, and Carlini and Wagner’s algorithms.

We have integrated our “feature squeezing” based detection framework in this toolbox. Formulating detecting adversarial examples as a binary classification task, we first construct a balanced dataset with equal number of legitimate and adversarial examples, and then split it into training and test subsets. A detection method has full access to the training set but no access to the labels of the test set. We measure the TPR and FPR on the test set as the benchmark detection results. Our Feature Squeezing functions as the detection baseline. Users can easily add more detection methods using our framework.

Besides, the tool comes with an interactive web-based visualization module adapted from our previous ADVERSARIAL-PLAYGROUND package. This module enables better understanding of the impact of attack algorithms on the resulting adversarial sample; users may specify attack algorithm parameters for a variety of attack types and generate new samples on-demand. The interface displays the resulting adversarial example as compared to the original, classification likelihoods, and the influence of a target model throughout layers of the network.

Citations

@inproceedings{Xu0Q18,
  author    = {Weilin Xu and
               David Evans and
               Yanjun Qi},
  title     = {Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks},
  booktitle = {25th Annual Network and Distributed System Security Symposium, {NDSS}
               2018, San Diego, California, USA, February 18-21, 2018},
  year      = {2018},
  crossref  = {DBLP:conf/ndss/2018},
  url       = {http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018\_03A-4\_Xu\_paper.pdf},
  timestamp = {Thu, 09 Aug 2018 10:57:16 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ndss/Xu0Q18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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Having troubl with our tools? Please contact Weilin and we’ll help you sort it out.

Feature Squeezing Mitigates and Detects Carlini-Wagner Adversarial Examples

Paper Arxiv

Abstract

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

Citations

@article{xu2017feature,
  title={Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples},
  author={Xu, Weilin and Evans, David and Qi, Yanjun},
  journal={arXiv preprint arXiv:1705.10686},
  year={2017}
}

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Having troubl with our tools? Please contact Weilin and we’ll help you sort it out.