Incremental learning for the detection and classification of GAN-generated images

TL;DR

This paper introduces an incremental learning approach to detect and classify GAN-generated images, enabling models to adapt to new types of generated data over time, which is crucial for real-world applications.

Contribution

It presents a novel incremental learning method specifically designed for GAN image detection and classification, addressing the challenge of evolving data distributions.

Findings

Effective discrimination of GAN-generated images with new GAN types

Model adapts to evolving data without retraining from scratch

High accuracy maintained across multiple GAN architectures

Abstract

Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones. In particular, human face generation achieved a stunning level of realism, opening new opportunities for the creative industry but, at the same time, new scary scenarios where such content can be maliciously misused. Therefore, it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia, possibly able to follow the evolution and continuous improvement of data in terms of quality and realism. In the last few years, several deep learning-based solutions have been proposed for this problem, mostly based on Convolutional Neural Networks (CNNs). Although results are good in controlled conditions, it is not clear how such proposals can adapt to real-world scenarios, where learning needs to continuously evolve as new types of generated data appear. In this work, we tackle this problem by proposing an approach based on incremental learning for the detection and classification of GAN-generated images. Experiments on a dataset comprising images generated by several GAN-based architectures show that the proposed method is able to correctly perform discrimination when new GANs are presented to the network