A-PixelHop: A Green, Robust and Explainable Fake-Image Detector
It addresses the need for efficient and robust fake-image detection, which is crucial for security and media integrity, though it appears incremental as it builds on existing detection approaches.
The paper tackled the problem of detecting CNN-generated fake images by proposing A-PixelHop, a method that achieves high detection performance with low computational complexity and small model size, outperforming state-of-the-art methods on CycleGAN-generated images and generalizing well to unseen models and datasets.
A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work. It has three advantages: 1) low computational complexity and a small model size, 2) high detection performance against a wide range of generative models, and 3) mathematical transparency. A-PixelHop is designed under the assumption that it is difficult to synthesize high-quality, high-frequency components in local regions. It contains four building modules: 1) selecting edge/texture blocks that contain significant high-frequency components, 2) applying multiple filter banks to them to obtain rich sets of spatial-spectral responses as features, 3) feeding features to multiple binary classifiers to obtain a set of soft decisions, 4) developing an effective ensemble scheme to fuse the soft decisions into the final decision. Experimental results show that A-PixelHop outperforms state-of-the-art methods in detecting CycleGAN-generated images. Furthermore, it can generalize well to unseen generative models and datasets.