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Mainly, two approaches were used to design fake colorized image detection systems. The first one uses traditional machine learning (ML) techniques that rely on hand-crafted features derived from images and used to differentiate actual and fake images. The second approach uses deep learning (DL) techniques as \u201cend to end\u201d systems that don\u2019t have to be supplied with such hand-crafted features, as they can learn the features from the image directly. This paper focuses on the various methods and techniques used in fake-colorized image detection. It may aid researchers in better understanding the benefits and drawbacks of existing technologies to develop more efficient systems in this field. <\/jats:p>","DOI":"10.1142\/s021946782350050x","type":"journal-article","created":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T04:34:01Z","timestamp":1662179641000},"source":"Crossref","is-referenced-by-count":1,"title":["Fake Colorized Image Detection Approaches: A Review"],"prefix":"10.1142","volume":"23","author":[{"given":"Khalid A.","family":"Salman","sequence":"first","affiliation":[{"name":"College of Computer Sciences and Information Technology, University of Anbar, Iraq"}]},{"given":"Khalid","family":"Shaker","sequence":"additional","affiliation":[{"name":"College of Computer Sciences and Information Technology, University of Anbar, Iraq"}]},{"given":"Sufyan","family":"Al-Janabi","sequence":"additional","affiliation":[{"name":"College of Computer Sciences and Information Technology, University of Anbar, Iraq"}]}],"member":"219","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"S021946782350050XBIB003","doi-asserted-by":"publisher","DOI":"10.1109\/ICIIECS.2015.7193253"},{"key":"S021946782350050XBIB004","doi-asserted-by":"publisher","DOI":"10.25046\/aj050347"},{"key":"S021946782350050XBIB005","first-page":"1","volume":"6013","author":"Guo Y.","year":"2018","journal-title":"IEEE Trans. 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