Abstract

Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics.

Highlights

  • With the popularity of digital cameras and smartphones, digital images have become increasingly widely used

  • Use the prior information at the pixel level of the inpainted area to guide and supervise the training of this deep neural network. en, we adjust and improve the network according to the shape of the inpainted data, including the size of the anchor scales and an improved method using the threshold of nonmaximum suppression, so that the model can generate the more accurate area of interest

  • Considering tampering images are often attacked by JPEG compression and image scaling, we test the robustness of our proposed approach in Tables 2 and 3

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Summary

Introduction

With the popularity of digital cameras and smartphones, digital images have become increasingly widely used. En, we adjust and improve the network according to the shape of the inpainted data, including the size of the anchor scales and an improved method using the threshold of nonmaximum suppression, so that the model can generate the more accurate area of interest.

Results
Conclusion
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