An Improved Algorithm for Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts

Le, Nhan; Retraint, Florent

doi:10.1109/access.2019.2938467

Cited by 19 publications

(22 citation statements)

References 46 publications

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“…1 shows the typical process of forgery localization methods using CFA artifacts. First, the green channel [36,37,39] or all color channels [35,38,40] are selected to estimate the re-interpolation kernel. Next, prediction residue is generated using the difference between the tampered and reinterpolated images by the estimated kernel.…”

Section: Related Workmentioning

confidence: 99%

“…In 2019, Le et al [39] introduced an improved forgery localization algorithm using demosaicing artifacts. They first explained why the demosaicing-based approach is less effective with JPEG compressed images.…”

Section: Related Workmentioning

confidence: 99%

“…The detail derivations for these two variances are outlined by [36] and [39]. According to ( 6) and ( 7), we can assume that σ A 2 is higher than σ T 2 in the presence of CFA demosaicing.…”

Section: Variance Of Prediction Residuementioning

confidence: 99%

“…When an image is tampered, forged regions exhibit demosaicing inconsistencies within authentic image regions. Accordingly, a number of studies have been conducted [35][36][37][38][39][40] to localize forged regions using demosaicing traces. However, the interpolation kernel for demosaicing is generally unknown.…”

Section: Introductionmentioning

confidence: 99%

“…These interpolation kernel types only use intra-channel information, and they are, therefore, inappropriate for demosaicing methods using inter-channel color information. To address this, least-squares-based approaches [38,39] are used to estimate the re-interpolation kernel. However, estimating one kernel for one image is not desirable because more than two interpolation kernels can exist for one spliced image.…”

Section: Introductionmentioning

confidence: 99%

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Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

2021

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Almost all image sensors measure only one color per pixel through the color filter array. Missing pixels are estimated using a demosaicing process. For this reason, a demosaiced image leaves a particular trace. When an image is manipulated or tampered, the demosaicing trace can be changed. This change can serve as a basic clue for detecting or localizing image tampering. Demosaicing pattern-based tampering localization algorithms require a re-interpolation process, and the prediction residue between the given image and the re-interpolated image is commonly used to localize tampered regions. However, the prediction residue is not always valid because the demosaicing interpolation kernel cannot be known, which deteriorates the localization performance. This paper presents an effective re-interpolation process using singular value decomposition for an unknown demosaicing method. First, the green channel of the given image is decomposed into four sub-images according to the Bayer pattern. For a small block of each subimage, the singular value decomposition is performed. The prediction residue is obtained by reconstructing the image block after removing the largest singular value. The feature to localize the forged regions is extracted by the logarithm ratio of the prediction residue variance. The proposed method does not require any statistical model for the extracted feature, because the prediction residue is more accurate than that of conventional methods. We perform intensive experiments for three test datasets and compare the proposed method with state-of-the-art tampering localization methods, the results of which indicate that the proposed scheme outperforms existing approaches.INDEX TERMS Image tampering localization, demosaicing trace, singular value decomposition, prediction residue, re-interpolation kernel, color filter array, image splicing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Variance Of Prediction Residuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

2021

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Face Manipulation Detection in Remote Operational Systems

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Mahfoudi

Trabelsi

et al. 2022

Handbook of Digital Face Manipulation and Detection

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In this chapter, we present the various categories of Face Manipulation and their use within different remote operational systems. We then use the example of remote identity document onboarding systems to illustrate how each category can be used in practice to compromise such a system. After a definition of the different Face Manipulation categories and the common algorithms used to produce them, we go through the various manipulation detection algorithms and common image and video forgery datasets. We then introduce some known counter-forensics methods that can be used by an attacker to avoid detection. Knowing the detection methods and the counter-forensic, we present how we can build up a safer system by using the correct methods at the correct time. But also how knowledge about the tampering process could be used to design the user experience to make the systems harder to compromise. We complete this review by the standardisation effort and legal aspect on the matter. And we conclude by discussing the remaining challenges and perspectives for better use of nowadays detection methods in practical usage.

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A Perspective View of Image and Video Forgery Techniques

Koshy

PraylaShyry

2023

Lecture Notes in Networks and Systems

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An Improved Algorithm for Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts

Cited by 19 publications

References 46 publications

Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

Face Manipulation Detection in Remote Operational Systems

A Perspective View of Image and Video Forgery Techniques

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