AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations

TL;DR

AstroSpy is a hybrid model that combines spectral and image features to effectively detect AI-generated fake astronomical images, outperforming existing methods and enhancing authenticity verification in astronomy.

Contribution

We introduce AstroSpy, a novel joint image-spectral model that improves detection of synthetic astronomical images over traditional single-modality approaches.

Findings

AstroSpy achieves higher accuracy than baseline models.

The model performs well in cross-domain detection.

It significantly reduces false positives in fake image identification.

Abstract

The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images, especially with advanced text-to-image models like Stable Diffusion producing highly realistic synthetic samples. Existing detection methods, primarily based on convolutional neural networks (CNNs) or spectral analysis, have limitations when used independently. We present AstroSpy, a hybrid model that integrates both spectral and image features to distinguish real from synthetic astronomical images. Trained on a unique dataset of real NASA images and AI-generated fakes (approximately 18k samples), AstroSpy utilizes a dual-pathway architecture to fuse spatial and spectral information. This approach enables AstroSpy to achieve superior performance in identifying authentic astronomical images. Extensive evaluations demonstrate AstroSpy's effectiveness and robustness, significantly outperforming baseline models in both in-domain and cross-domain tasks, highlighting its potential to combat misinformation in astronomy.