They achieved good color modification detection performance using an advanced intermediate value counting (AIVC) algorithm. They designed a color modification detection algorithm based on the fact that color filter array (CFA) patterns change if the color of a digital image is modified. However, hue and white balance adjustments are included in this definition. According to this definition, brightness adjustment, which modifies the luminance of an image, is not a color modification attack. They defined a color modification attack as a change in the ratio between red, blue, and green channels. introduced a basic color modification detection method. Forensic approaches that can detect color modification have not been extensively studied.
It is often exploited to obfuscate a person by changing their face color, eliminate criminal evidence by modifying the color of a car used in a crime, or mislead customers by changing the color of a product. Ĭolor modification is one of the commonly used image forgery techniques. There have been several studies on detecting various image forgery techniques, such as copy-move, image splicing, scaling, rotation, blurring, contrast change, and color modification. If we can uncover evidence indicating image alterations, we can conclude that an image has been forged. A trace of image manipulations can be used as a clue for detecting altered images. Therefore, developing reliable image forgery detection methods to determine the authenticity of images has become an important issue. Furthermore, it is difficult to verify the authenticity of images using only the human eye. Using image forgery techniques requires minimal expertise because digitized images are easily replicated or manipulated. Compared to the conventional method, our method provides superior color modification detection performance. Experimental results demonstrate that the proposed method generates accurate estimation results for detecting color modification. Additionally, changed color local regions can be efficiently detected using the proposed algorithm. We define a variance ratio measurement to quantify the level of color modification. For color difference images, we execute a wavelet transform and use the highest frequency subband to calculate variances.
The color difference model is used to emphasize the differences between the original and interpolated samples. Therefore, we present a novel algorithm for color modification estimation using the variance ratio of color difference images in the wavelet domain. It is assumed that the variance of original samples is greater than that of the interpolated samples. 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.
If the color of a digital image is modified, the locations of the interpolated and original samples may be changed. It can be used to eliminate criminal evidence in various ways, such as modifying the color of a car used in a crime. Color modification is one of the popular image forgery techniques.