  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.
