Honey Authentication with Machine Learning Augmented Bright-Field Microscopy

2018-12-28 · Peter He, Alexis Gkantiragas, Gerard Glowacki

Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.

📄 PDF Abstract BibTeX arXiv:1901.00516

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learning

HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images

2016-11-11 · Mor Ohana, Orr Dunkelman, Stuart Gibson, Margarita Osadchy

One of the main challenges faced by Biometric-based authentication systems is the need to offer secure authentication while maintaining the privacy of the biometric data. Previous solutions, such as Secure Sketch and Fuz…

Unsupervised Representations of Pollen in Bright-Field Microscopy

2019-08-05 · Peter He, Gerard Glowacki, Alexis Gkantiragas

We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification…

Clustering

Targeted Honeyword Generation with Language Models

2022-08-15 · FangYi Yu, Miguel Vargas Martin

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. The major difficulty is how to produce honeywords that are difficult to distinguish from real passwords. Although the ge…

Comparison of Machine Learning Models in Food Authentication Studies

2019-05-17 · Manokamna Singh, Katarina Domijan

The underlying objective of food authentication studies is to determine whether unknown food samples have been correctly labelled. In this paper we study three near infrared (NIR) spectroscopic datasets from food samples…

BIG-bench Machine Learning Dimensionality Reduction General Classification Variable Selection

HoneyModels: Machine Learning Honeypots

2022-02-21 · Ahmed Abdou, Ryan Sheatsley, Yohan Beugin, Tyler Shipp 외

Machine Learning is becoming a pivotal aspect of many systems today, offering newfound performance on classification and prediction tasks, but this rapid integration also comes with new unforeseen vulnerabilities. To har…

BIG-bench Machine Learning Computational Efficiency