Trustworthy Machine Learning · Trustworthy machine learning

The website http://TrustworthyMachineLearning.org/ introduces updates of a suite of tools we have designed for making machine learning secure and trustworthy. This project involves toolboxes for five main tasks (organized as entries in the navigation menu). Feel free to submit pull requests when you find my typos.

Scope of problems our tools aim to tackle

Classifiers based on machine learning algorithms have shown promising results for many security tasks including malware classification and network intrusion detection, but classic machine learning algorithms are not designed to operate in the presence of adversaries. Intelligent and adaptive adversaries may actively manipulate the information they present in attempts to evade a trained classifier, leading to a competition between the designers of learning systems and attackers who wish to evade them. This project is developing automated techniques for predicting how well classifiers will resist the evasions of adversaries, along with general methods to automatically harden machine-learning classifiers against adversarial evasion attacks.

Important tasks

At the junction between machine learning and computer security, this project involves toolboxes for five main task as shown in the following table. Our system aims to allow a classifier designer to understand how the classification performance of a model degrades under evasion attacks, enabling better-informed and more secure design choices. The framework is general and scalable, and takes advantage of the latest advances in machine learning and computer security.

timeline

We categorize the topics into a list of subtasks and list our selected works in the following table:

No.	Tool Category	~~~~~~~~~Paper~Title~~~~~~	Venues	Software
1	Evade NLP Machine Learning	TextAttack: A Framework for Adversarial Attacks in Natural Language Processing	EMNLP2020	GitHub
2	Evade Machine Learning	Automatically Evading Classifiers, Case Study on PDF Malware Classifiers	NDSS16	GitHub
3	Evade NLP Machine Learning	Black-box Generation of Adversarial Text Sequences to Fool Deep Learning Classifiers	DeepSecureWkp18	GitHub
4	Detect Adversarial Attacks	Feature Squeezing- Detecting Adversarial Examples in Deep Neural Networks	NDSS18	GitHub
5	Defense against Adversarial Attacks	DeepCloak- Masking Deep Neural Network Models for Robustness against Adversarial Samples	ICLRwkp17	GitHub
6	Visualize Adversarial Attacks	Adversarial-Playground- A Visualization Suite for Adversarial Samples	VizSec17	GitHub
7	Theorems of Adversarial Examples	A Theoretical Framework for Robustness of (Deep) Classifiers Against Adversarial Samples	ICLRw17
8	Trustworthy via Interpretation	Deep Motif Dashboard	ICLRw2017

Contact

Have questions or suggestions? Feel free to ask me on Twitter or email me.

Thanks for reading!