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Forensic analysis and detection using polycolor model binary pattern for colorized images
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AI
DOI：10.1007/s11042-023-16675-1.png)
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Abstract
En 中文
Image recoloring is used to colorize historic grayscale images. Recoloring gives new life to grayscale images with realistic colors. Several techniques exist that can colorize grayscale images. Sometimes attackers create a realistic fake image using colorization techniques. Consequently, there is a need for some technique that can differentiate between natural color and colorized image to stop the misuse of the colorization techniques. In this paper, a Polycolor Model Binary Pattern (PMBP) is proposed to extract robust internal statistical features. Several color models such as RGB, YUV, YCbCr, and HSV are considered to fetch crucial statistical information from an image. A novel Polycolor Model Binary Pattern is formed using the effective channels of several color models. The proposed method gives promising results on non-compressed colorized images as well as on highly compressed colorized images. The efficacy of the proposed detection technique is verified on three fully automated deep learning-based colorization techniques. Three diverse image databases are used to assess the performance. Linear Discriminant Analysis classifier is utilized to classify natural color and colorized images from extracted features, whereas the inverse of the covariance matrix is calculated by using the Moore-Penrose Pseudo Inverse Matrix (MPPM).
Keywords:
Image forensics
Colorized image detection
Polycolor model binary pattern
Journal
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3
Papers:
1.9W
Citations:
3.2W