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Image forgery localization based on fully convolutional network with noise feature

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Abstract

To detect the maliciously tampered region in digital images, this paper proposes an image forgery localization method based on a fully convolutional network (FCN). In the pre-processing phase of the network, noise features are used to adequately expose the subtle changes in the image caused by manipulation operations, thus enhancing the generalization ability of the network. The convolutional layer is used in a fully convolutional network instead of the fully connected layer to generate a pixel-wise prediction. In addition, the region proposal network used in object detection is added to improve the robustness. Experiments on standard datasets show that our method can accurately locate the tampered regions of images and improve generalization ability and robustness.

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  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 201306, China

    Qiuxu Liu, Hongjiao Li & Zheng Liu

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  1. Qiuxu Liu
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  2. Hongjiao Li
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  3. Zheng Liu
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Correspondence to Hongjiao Li.

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Liu, Q., Li, H. & Liu, Z. Image forgery localization based on fully convolutional network with noise feature. Multimed Tools Appl 81, 17919–17935 (2022). https://doi.org/10.1007/s11042-022-12758-7

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