Fake Colorized Image Detection
Fake Colorized Image Detection
复制标题
假彩色图像检测
DOI：
10.1109/tifs.2018.2806926
复制
发表时间：
2018-01
影响因子：
6.8
通讯作者：
Rui Wang
中科院分区：
文献类型：
--
作者：
Yuanfang Guo;Xiaochun Cao;Wei Zhang;Rui Wang
图像取证旨在检测对数字图像的操纵。目前，拼接检测、复制-移动检测和图像修饰检测是研究人员非常关注的问题。然而，图像编辑技术随着时间的推移而发展。一种新兴的图像编辑技术是着色，将灰度图像用逼真的颜色着色。不幸的是，这种技术也可能被有意地应用于某些图像，以混淆对象识别算法。据我们所知，目前还没有发明任何法医技术来识别图像是否被着色。我们观察到，与自然图像相比，由三种最先进的方法生成的彩色图像在色调和饱和度通道上具有统计差异。此外，我们还观察到暗通道和亮通道的统计不一致，因为着色过程不可避免地会影响暗通道和亮通道的值。基于我们的观察，即在色调、饱和度、暗、亮通道中的潜在痕迹，我们提出了两种简单而有效的伪彩色图像检测方法：基于直方图的伪彩色图像检测和基于特征编码的伪彩色图像检测。实验结果表明，这两种方法对多种最先进的着色方法都表现出良好的性能。
Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.
登录
查看更多内容
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
DOI：
10.1109/apsipa.2016.7820758
发表时间：
2016-12
期刊：
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)
影响因子：
--
作者：
Yuanman Li;Jiantao Zhou
通讯作者：
Yuanman Li;Jiantao Zhou
DOI：
10.1109/cvpr.2007.383266
发表时间：
2007-06
期刊：
2007 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Florent Perronnin;C. Dance
通讯作者：
Florent Perronnin;C. Dance
DOI：
10.1109/icccnt56998.2023.10306417
发表时间：
2022-02
期刊：
2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
影响因子：
--
作者：
Gilad Cohen;Raja Giryes
通讯作者：
Gilad Cohen;Raja Giryes
影响因子：
10.6
作者：
Xiaowu Chen;Jianwei Li;Dongqing Zou;Qinping Zhao
通讯作者：
Xiaowu Chen;Jianwei Li;Dongqing Zou;Qinping Zhao