TextureCrop: Enhancing Synthetic Image Detection through Texture-based Cropping

Autor:	Konstantinidou, Despina, Koutlis, Christos, Papadopoulos, Symeon
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Generative AI technologies produce hyper-realistic imagery that can be used for nefarious purposes such as producing misleading or harmful content, among others. This makes Synthetic Image Detection (SID) an essential tool for defending against AI-generated harmful content. Current SID methods typically resize input images to a fixed resolution or perform center-cropping due to computational concerns, leading to challenges in effectively detecting artifacts in high-resolution images. To this end, we propose TextureCrop, a novel image pre-processing technique. By focusing on high-frequency image parts where generation artifacts are prevalent, TextureCrop effectively enhances SID accuracy while maintaining manageable memory requirements. Experimental results demonstrate a consistent improvement in AUC across various detectors by 5.7% compared to center cropping and by 14% compared to resizing, across high-resolution images from the Forensynths and Synthbuster datasets. Comment: 17 pages, 6 images
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.15500 Zobrazit plný text záznamu View this record from Arxiv