Image Inpainting Forgery Detection: A Review
In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/10/2/42 |
| _version_ | 1827343421602267136 |
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| author | Adrian-Alin Barglazan Remus Brad Constantin Constantinescu |
| author_facet | Adrian-Alin Barglazan Remus Brad Constantin Constantinescu |
| author_sort | Adrian-Alin Barglazan |
| collection | DOAJ |
| description | In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images. |
| first_indexed | 2024-03-07T22:26:59Z |
| format | Article |
| id | doaj.art-c3b7c95fc2d74579913f4e66a56dbfe8 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-07T22:26:59Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c3b7c95fc2d74579913f4e66a56dbfe82024-02-23T15:22:45ZengMDPI AGJournal of Imaging2313-433X2024-02-011024210.3390/jimaging10020042Image Inpainting Forgery Detection: A ReviewAdrian-Alin Barglazan0Remus Brad1Constantin Constantinescu2Faculty of Engineering, Computer Science, “Lucian Blaga” University of Sibiu, 550024 Sibiu, RomaniaFaculty of Engineering, Computer Science, “Lucian Blaga” University of Sibiu, 550024 Sibiu, RomaniaFaculty of Engineering, Computer Science, “Lucian Blaga” University of Sibiu, 550024 Sibiu, RomaniaIn recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.https://www.mdpi.com/2313-433X/10/2/42image inpaintingobject removal detectionforensic forgery |
| spellingShingle | Adrian-Alin Barglazan Remus Brad Constantin Constantinescu Image Inpainting Forgery Detection: A Review Journal of Imaging image inpainting object removal detection forensic forgery |
| title | Image Inpainting Forgery Detection: A Review |
| title_full | Image Inpainting Forgery Detection: A Review |
| title_fullStr | Image Inpainting Forgery Detection: A Review |
| title_full_unstemmed | Image Inpainting Forgery Detection: A Review |
| title_short | Image Inpainting Forgery Detection: A Review |
| title_sort | image inpainting forgery detection a review |
| topic | image inpainting object removal detection forensic forgery |
| url | https://www.mdpi.com/2313-433X/10/2/42 |
| work_keys_str_mv | AT adrianalinbarglazan imageinpaintingforgerydetectionareview AT remusbrad imageinpaintingforgerydetectionareview AT constantinconstantinescu imageinpaintingforgerydetectionareview |