An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Wang, Xinyi; Wang, He; Niu, Shaozhang

doi:10.1155/2020/8892989

Cited by 12 publications

(8 citation statements)

References 38 publications

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“…Degradation of acquired signals caused by Poisson noise is a common phenomenon in applications such as biomedical imaging, night vision, and astronomy [17,18]. Therefore, Poisson noise removal is especially important for further processing such as image classification and recognition.…”

Section: Methodsmentioning

confidence: 99%

Digital Image Restoration Based on Multicontour Batch Scanning

et al. 2022

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In order to explore the problem of digital image restoration, the authors propose a research on digital image restoration based on multicontour batch scanning. This method recommends key technical problems and solutions based on information represented by multicontour batch scans, exploring research in digital image restoration. Research has shown that the research on digital image restoration based on multicontour batch scanning is about 40% more efficient than traditional methods. Aiming at the new application of digital image inpainting, the application of image inpainting in image compression is studied in depth, and the technical principles of image inpainting and image compression are complemented.

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Section: Methodsmentioning

confidence: 99%

Digital Image Restoration Based on Multicontour Batch Scanning

et al. 2022

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“…Image comprehension encompasses various tasks, including feature extraction, object identification, object recognition, image cleaning, and image transformation. CNNs have consistently performed well in various computer vision tasks that previously needed a visual inspection [15,16]. As a result of these astounding results, academics have begun to use CNNs to solve picture forensic challenges.…”

Section: Digital Forensics and Computer Visionmentioning

confidence: 98%

Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Latif

Xiao

et al. 2021

Security and Communication Networks

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In parallel with the development of various emerging fields such as computer vision and related technologies, e.g., iris identification and glaucoma detection, criminals are developing their methods. It is the foremost reason for the blindness of human beings that affects the eye’s optic nerve. Fundus photography is carried out to examine this eye disease. Medical experts evaluate fundus photographs, which is a time-consuming visual inspection. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features nuanced by the underlying segmentation methods. Convolutional neural networks (CNNs) are powerful tools for solving image classification tasks, as they can learn highly discriminative features from raw pixel intensities. However, their applicability to medical image analysis is limited by the nonavailability of large sets of annotated data required for training. In this work, we aim to accelerate this process using a computer-aided diagnosis of this severe disease with the help of transfer learning based on deep convolutional neural networks. We have suggested the Inception V-3 approach for image classification based on convolution neural networks. Our developed model has the potential to address this CNN model’s problem of classification accuracy, and with imaging data, our proposed method outperforms recent state-of-the-art approaches. The case study for digital forensics is an essential component of emerging technologies, and hence glaucoma detection plays a vital role in it.

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“…Commonly used editing tools and operations in photoshop 0.99 (AUC) [145] ResNet; image residuals Remove; CNN inpainting 97.97 (precision) [146] Multibranch CNN architecture Copy-move 0.920 (F1-score) [147] RGB-N, MSCNNs, DCNNs Splicing, copy-move 0.7328 (precision) [148] Feature pyramid network, stagewise-weighted cross-entropy Loss function JPEG compression, scaling 0.9967 (F1-score) [149] CNN with CRF-based attention model Splicing, copy-move 0.804 (F1-score) [150] Dense self-attention encoders Copy-move 0.883 (AUC) et al [151] proposed a mask regional convolutional neural network (Mask R-CNN) approach by adjusting the sizes of the anchor scales due to the inpainting images and then by replacing the original non-maximum suppression single threshold with an improved non-maximum suppression (NMS) to reduce the missed detection areas and improve detection accuracy. Lu et al [128] proposed an image inpainting forgery detection method based on LSTM-CNN.…”

Section: Deepmentioning

confidence: 99%

Overview of Image Inpainting and Forensic Technology

Liu

Bukhari

2022

Security and Communication Networks

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At present, digital image is the most significant information carrier on the Internet, which provides convenience for people to exchange information. With the progress of image processing theory, digital image inpainting technology has also developed rapidly. It not only enhances the image expression but also provides tools for image forgery. Misusing the tools may trigger a crisis of trust in the image. To eliminate the negative impact of image forgery, many scholars have conducted in-depth research on it and proposed a series of detection methods called image forensics for institutions or communities to identify such forged images. Current forensic technology still has many limitations, such as relying on specific features and data distribution which makes forensic technology lag behind image inpainting technology. So far, academia still lacks a unified understanding of image forgery and detection technology, and the research architecture of inpainting and forensic technology is not clear. This article reviews the development of image inpainting and forensic technology and systematically summarizes and scientifically classifies the current research work. Finally, the forensic technology is summarized and the future research direction is prospected to guide subsequent scholars to further promote the progress of image forensic technology.

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An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Cited by 12 publications

References 38 publications

Digital Image Restoration Based on Multicontour Batch Scanning

Digital Image Restoration Based on Multicontour Batch Scanning

Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Overview of Image Inpainting and Forensic Technology

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