DeepAI AI Chat
Log In Sign Up

T-GD: Transferable GAN-generated Images Detection Framework

by   Hyeonseong Jeon, et al.

Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detecting these GAN-generated images (GAN-images) becomes increasingly challenging due to the significant reduction of underlying artifacts and specific patterns. The absence of such traces can hinder detection algorithms from identifying GAN-images and transferring knowledge to identify other types of GAN-images as well. In this work, we present the Transferable GAN-images Detection framework T-GD, a robust transferable framework for an effective detection of GAN-images. T-GD is composed of a teacher and a student model that can iteratively teach and evaluate each other to improve the detection performance. First, we train the teacher model on the source dataset and use it as a starting point for learning the target dataset. To train the student model, we inject noise by mixing up the source and target datasets, while constraining the weight variation to preserve the starting point. Our approach is a self-training method, but distinguishes itself from prior approaches by focusing on improving the transferability of GAN-image detection. T-GD achieves high performance on the source dataset by overcoming catastrophic forgetting and effectively detecting state-of-the-art GAN-images with only a small volume of data without any metadata information.


page 1

page 2

page 5

page 12

page 13


Student's t-Generative Adversarial Networks

Generative Adversarial Networks (GANs) have a great performance in image...

Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images

The rapid progression of Generative Adversarial Networks (GANs) has rais...

P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection

One-class novelty detection is to identify anomalous instances that do n...

Misleading Deep-Fake Detection with GAN Fingerprints

Generative adversarial networks (GANs) have made remarkable progress in ...

Detection, Attribution and Localization of GAN Generated Images

Recent advances in Generative Adversarial Networks (GANs) have led to th...

Towards the Detection of Diffusion Model Deepfakes

Diffusion models (DMs) have recently emerged as a promising method in im...

Artificial GAN Fingerprints: Rooting Deepfake Attribution in Training Data

Photorealistic image generation is progressing rapidly and has reached a...