# Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning

Source: [Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning](<https://www.sota2.com/research/paper/68fa66d01e209131cea12891>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:59:21.344Z

Authors: Xinsheng Xuan, Bo Peng, Wei Wang, Jing Dong

Published: 2019-12-10T08:19:37.000Z

## Abstract

Recently, generated images could reach very high quality, even human eyes could not tell them apart from real images. Although there are already some methods for detecting generated images in current forensic community, most of these methods are used to detect a single type of generated images. The new types of generated images are emerging one after another, and the existing detection methods cannot cope well. These problems prompted us to propose a scalable framework for multi-class classification based on deep metric learning, which aims to classify the generated images finer. In addition, we have increased the scalability of our framework to cope with the constant emergence of new types of generated images, and through fine-tuning to make the model obtain better detection performance on the new type of generated data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.11082>)

[PDF](<https://arxiv.org/pdf/1912.11082.pdf>)