Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance

05/20/2021
by   Jack W. Stokes, et al.
0

Recent research has successfully demonstrated new types of data poisoning attacks. To address this problem, some researchers have proposed both offline and online data poisoning detection defenses which employ machine learning algorithms to identify such attacks. In this work, we take a different approach to preventing data poisoning attacks which relies on cryptographically-based authentication and provenance to ensure the integrity of the data used to train a machine learning model. The same approach is also used to prevent software poisoning and model poisoning attacks. A software poisoning attack maliciously alters one or more software components used to train a model. Once the model has been trained it can also be protected against model poisoning attacks which seek to alter a model's predictions by modifying its underlying parameters or structure. Finally, an evaluation set or test set can also be protected to provide evidence if they have been modified by a second data poisoning attack. To achieve these goals, we propose VAMP which extends the previously proposed AMP system, that was designed to protect media objects such as images, video files or audio clips, to the machine learning setting. We first provide requirements for authentication and provenance for a secure machine learning system. Next, we demonstrate how VAMP's manifest meets these requirements to protect a machine learning system's datasets, software components, and models.

READ FULL TEXT

Authors

page 1

page 4

03/10/2022

Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method

Biometric authentication methods, representing the "something you are" s...
05/28/2019

Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

Mouse dynamics is a potential means of authenticating users. Typically, ...
02/15/2021

A Data Quality-Driven View of MLOps

Developing machine learning models can be seen as a process similar to t...
01/26/2022

Phishing Attacks Detection – A Machine Learning-Based Approach

Phishing attacks are one of the most common social engineering attacks t...
05/18/2020

An Overview of Privacy in Machine Learning

Over the past few years, providers such as Google, Microsoft, and Amazon...
01/22/2020

AMP: Authentication of Media via Provenance

Advances in graphics and machine learning have led to the general availa...
08/30/2021

A Novel Approach to Detect Phishing Attacks using Binary Visualisation and Machine Learning

Protecting and preventing sensitive data from being used inappropriately...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.