ar X iv : 1 70 6 . 07 84 2 v 3 [ cs . C V ] 1 6 O ct 2 01 7 IMAGE FORGERY LOCALIZATION BASED ON MULTI - SCALE CONVOLUTIONAL NEURAL NETWORKS
نویسندگان
چکیده
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 smallscale and large-scale analyses, the segmentation-based multiscale 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.
منابع مشابه
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
3 Introduction 3 Materials and Methods 5 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Deep Neural Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Experimental Setup . . . . ...
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