Estimation of color modification in digital images by CFA pattern change

Choi, Chang-Hee; Lee, Hae-Yeoun; Lee, Heung-Kyu

doi:10.1016/j.forsciint.2012.12.014

Cited by 46 publications

(23 citation statements)

References 17 publications

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“…2, RGB values change in a periodic manner according to hue changes. If the color of an image is The conventional algorithm for estimating color modification [15] exploits the color sample changes caused by color modification. This method is based on the idea that the trace of a CFA pattern, which is closely related to color information, changes when the color of an image is modified.…”

Section: Conventional Color Change Detection Methodsmentioning

confidence: 99%

“…We then calculate two variance ratios using (13) and (14). Next, the estimation measurement, V E (H) for detecting color modification is obtained using (15). V E (H) is calculated by repeating the same process until H becomes 360°.…”

Section: Hhmentioning

confidence: 99%

“…If we can uncover evidence indicating image alterations, we can conclude that an image has been forged. There have been several studies on detecting various image forgery techniques, such as copy-move [3][4][5], image splicing [6][7][8], scaling [9,10], rotation [11], blurring [12,13], contrast change [14], and color modification [15].…”

Section: Introductionmentioning

confidence: 99%

“…Forensic approaches that can detect color modification have not been extensively studied. In 2013, Choi et al introduced a basic color modification detection method [15]. They defined a color modification attack as a change in the ratio between red, blue, and green channels.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet-based color modification detection based on variance ratio

Jeon

Eom

2018

J Image Video Proc.

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Color modification is one of the popular image forgery techniques. It can be used to eliminate criminal evidence in various ways, such as modifying the color of a car used in a crime. If the color of a digital image is modified, the locations of the interpolated and original samples may be changed. Because the original and interpolated pixels have different statistical characteristics, these differences can serve as a basic clue for estimating the degree of color modification. It is assumed that the variance of original samples is greater than that of the interpolated samples. Therefore, we present a novel algorithm for color modification estimation using the variance ratio of color difference images in the wavelet domain. The color difference model is used to emphasize the differences between the original and interpolated samples. For color difference images, we execute a wavelet transform and use the highest frequency subband to calculate variances. We define a variance ratio measurement to quantify the level of color modification. Additionally, changed color local regions can be efficiently detected using the proposed algorithm. Experimental results demonstrate that the proposed method generates accurate estimation results for detecting color modification. Compared to the conventional method, our method provides superior color modification detection performance.

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Section: Conventional Color Change Detection Methodsmentioning

confidence: 99%

Section: Hhmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wavelet-based color modification detection based on variance ratio

Jeon

Eom

2018

J Image Video Proc.

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“…The specific local image manipulation considered in this paper, hue modification, distorts artifacts of demosaicing and neighboring dependencies. Based on this fact, the pioneering work in [6] analyzes demosaicing artifacts and then estimates hue modification. Based on the observation that an interpolated value is bigger than the minimum and smaller than the maximum of its neighborhood, on the green channel the number of pixel values unsatisfying this condition should be the majority of pixel which are originally captured in this channel and a minority of interpolated pixels, resulting a big ratio between two quantities.…”

Section: Introductionmentioning

confidence: 99%

Hue Modification Localization By Pair Matching

Phan

Vascotto

Boato

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Hue modification is the adjustment of hue property on color images. Conducting hue modification on an image is trivial, and it can be abused to falsify opinions of viewers. Since shapes, edges or textural information remains unchanged after hue modification, this type of manipulation is relatively hard to be detected and localized. Since small patches inherit the same Color Filter Array (CFA) configuration and demosaicing, any distortion made by local hue modification can be detected by patch matching within the same image. In this paper, we propose to localize hue modification by means of a Siamese neural network specifically designed for matching two inputs. By crafting the network outputs, we are able to form a heatmap which potentially highlights malicious regions. Our proposed method deals well not only with uncompressed images but also with the presence of JPEG compression, an operation usually hindering the exploitation of CFA and demosaicing artifacts. Experimental evidences corroborate the effectiveness of the proposed method.

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Camera‐Based Image Forgery Detection

Ho¹,

Li²

2015

Handbook of Digital Forensics of Multimedia Data and Devices

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Estimation of color modification in digital images by CFA pattern change

Cited by 46 publications

References 17 publications

Wavelet-based color modification detection based on variance ratio

Wavelet-based color modification detection based on variance ratio

Hue Modification Localization By Pair Matching

Camera‐Based Image Forgery Detection

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