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arXiv · 1706.07842

Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

Yaqi Liu·Qingxiao Guan·Xianfeng Zhao·Yun Cao
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Abstract

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.

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Yaqi Liu, Qingxiao Guan, Xianfeng Zhao, Yun Cao. 2018-02-07. Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks. https://doi.org/10.1109/tgrs.2018.2848473

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