This paper introduces the Multi-directional Similarity Network (MSN), a novel deep learning approach designed for accurate and efficient detection of copy-move image forgeries, including those created by both manual manipulation and advanced deep generative networks. It addresses limitations in existing deep detection models by improving feature representation and localization, while also presenting a new benchmark for deep-synthesized copy-move forgeries.

This paper introduces a novel method for detecting images generated by Generative Adversarial Networks (GANs) by analyzing their unique frequency-domain 'fingerprints'. It leverages the 2D Discrete Fourier Transform to reveal subtle periodic artifacts, achieving significantly higher accuracy than spatial-domain methods when combined with a ResNet50 classifier.