A-PixelHop: A Green, Robust and Explainable Fake-Image Detector

• URL: http://arxiv.org/abs/2111.04012v1
• Date: Sun, 7 Nov 2021 06:31:26 GMT
• Title: A-PixelHop: A Green, Robust and Explainable Fake-Image Detector
• Authors: Yao Zhu, Xinyu Wang, Hong-Shuo Chen, Ronald Salloum, C.-C. Jay Kuo
• Abstract summary: 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.
• Score: 27.34087987867584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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