In the era of generative artificial intelligence (AI) applications, the challenge of distinguishing real from AI-generated synthetic images is critical for ensuring security and information authenticity. Our study presents a cutting-edge method for detecting synthetic images, combining a pretrained Autoencoder with Fourier transform techniques to extract unique image fingerprints. The main idea behind the use of the Autoencoder is that it would fail to reconstruct unnatural features in AI-generated images. The occurrence of such failure gives rise to an isolated attribute referred to as residual noise, which serves as an indicator of the image generation process and significantly improves the efficacy of detecting counterfeit images. This advancement results in an impressive improvement, yielding a +22.29% increase in accuracy and a +16.72% rise in AUC compared to state-of-the-art competitors. This advancement not only demonstrates the efficacy of our approach but also highlights its potential for widespread application in areas requiring robust security and information verification measures.