OCAN
Fraud detector
A framework for detecting fraud using a novel neural network approach that learns from benign user data
OCAN: One-Class Adversarial Nets for Fraud Detection
24 stars
1 watching
10 forks
Language: Python
last commit: about 8 years agoLinked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
| An implementation of a graph neural network-based fraud detector designed to counter camouflaged fraudsters | 250 | |
| A toolbox for building and comparing graph neural network-based fraud detection models | 698 | |
| Develops a survival analysis-based model to detect fraud early | 34 | |
| Develops an object segmentation algorithm to detect camouflaged objects in images with varying backgrounds and contexts. | 20 | |
| Develops and evaluates machine learning models for detecting financial fraud | 195 | |
| A deep learning framework for object detection tasks using a novel neural network architecture | 355 | |
| A toolbox for unsupervised graph-based fraud detection using multiple algorithms and techniques | 131 | |
| Develops a machine learning model to classify and rank customs fraud cases based on transaction-level data and tree-based features | 61 | |
| Reproduce experiments and results from a research paper on fraud detection using machine learning algorithms. | 4 | |
| Maps deception detection techniques to the ATT&CK framework and provides documentation for security professionals | 287 | |
| An approach to detect noise in labels used with deep neural networks during training | 77 | |
| A Python library for detecting outliers, adversarial examples, and data drift in various types of data | 2,262 | |
| Developing and testing AI algorithms to detect synthetic images generated by new media synthesis models like StyleGAN3. | 129 | |
| A Python interface to YOLO object detection software using Darknet | 27 | |
| A Python-based object detection framework utilizing transformers and computer vision techniques to detect salient objects in RGB-thermal images | 16 |