Overview of Image Inpainting and Forensic Technology

Liu, Kai; Li, Junke; Bukhari, S. Sabahat H.

doi:10.1155/2022/9291971

Cited by 8 publications

(3 citation statements)

References 178 publications

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“…All the algorithms which tackled this problem begin with the assumption that there must be some correlation between the pixels presented inside the image, either from a statistical or from a geometrical perspective. This work differs from the one in [6], by its deep focus on inpainting methods exclusively.…”

Section: Inpainting Methodsmentioning

confidence: 94%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2023

Preprint

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In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.

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Section: Inpainting Methodsmentioning

confidence: 94%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2023

Preprint

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“…This technique has been widely applied in various fields, including medicine, military, and video processing, among others [ 1 , 2 , 3 , 4 , 5 , 6 ]. The early image inpainting methods were mainly based on traditional image processing techniques, such as texture synthesis, patch-based methods, and exemplar-based methods.…”

Section: Introductionmentioning

confidence: 99%

Inpainting with Separable Mask Update Convolution Network

Gong

Luo

et al. 2023

Sensors

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Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this challenge, the paper proposes a novel approach called separable mask update convolution. This technique automatically learns and updates the mask, which represents the missing area, to better control the influence of invalid information within the mask area on the restoration results. Furthermore, this convolution method reduces the number of network parameters and the size of the model. The paper also introduces a regional normalization technique that collaborates with separable mask update convolution layers for improved feature extraction, thereby enhancing the quality of the restored image. Experimental results demonstrate that the proposed method performs well in restoring images with large missing areas and outperforms state-of-the-art image inpainting methods significantly in terms of image quality.

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“…As the future Internet officially ushers in the second stage of its development, image inpainting will face a huge challenge. This technique has already been applied in different fields, such as medicine, the military, and video processing, among others [1][2][3][4][5][6]. Traditional methods for image inpainting often have some defects.…”

Section: Introductionmentioning

confidence: 99%

Image Inpainting with Parallel Decoding Structure for Future Internet

et al. 2023

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Image inpainting benefits much from the future Internet, but the memory and computational cost in encoding image features in deep learning methods poses great challenges to this field. In this paper, we propose a parallel decoding structure based on GANs for image inpainting, which comprises a single encoding network and a parallel decoding network. By adding a diet parallel extended-decoder path for semantic inpainting (Diet-PEPSI) unit to the encoder network, we can employ a new rate-adaptive dilated convolutional layer to share the weights to dynamically generate feature maps by the given dilation rate, which can effectively decrease the number of convolutional layer parameters. For the decoding network composed of rough paths and inpainting paths, we propose the use of an improved CAM for reconstruction in the decoder that results in a smooth transition at the border of defective areas. For the discriminator, we substitute the local discriminator with a region ensemble discriminator, which can attack the restraint of only the recovering square, like areas for traditional methods with the robust training of a new loss function. The experiments on CelebA and CelebA-HQ verify the significance of the proposed method regarding both resource overhead and recovery performance.

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Overview of Image Inpainting and Forensic Technology

Cited by 8 publications

References 178 publications

Image Inpainting Forgery Detection: A Review

Image Inpainting Forgery Detection: A Review

Inpainting with Separable Mask Update Convolution Network

Image Inpainting with Parallel Decoding Structure for Future Internet

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