Image forgery detection a survey of recent deep-learning approaches
CC BY
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s11042-022-13797-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8330 |
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oai:localhost:PNK-83302023-04-26T04:17:30Z Image forgery detection a survey of recent deep-learning approaches Marcello, Zanardelli Fabrizio, Guerrini Riccardo, Leonardi DeepFake DL CC BY In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. This survey is especially timely because deep learning powered techniques appear to be the most relevant right now, since they give the best overall performances on the available benchmark datasets. 2023-04-26T04:17:30Z 2023-04-26T04:17:30Z 2022 Book https://link.springer.com/article/10.1007/s11042-022-13797-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8330 en application/pdf Springer |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
DeepFake DL |
| spellingShingle |
DeepFake DL Marcello, Zanardelli Fabrizio, Guerrini Riccardo, Leonardi Image forgery detection a survey of recent deep-learning approaches |
| description |
CC BY |
| format |
Book |
| author |
Marcello, Zanardelli Fabrizio, Guerrini Riccardo, Leonardi |
| author_facet |
Marcello, Zanardelli Fabrizio, Guerrini Riccardo, Leonardi |
| author_sort |
Marcello, Zanardelli |
| title |
Image forgery detection a survey of recent deep-learning approaches |
| title_short |
Image forgery detection a survey of recent deep-learning approaches |
| title_full |
Image forgery detection a survey of recent deep-learning approaches |
| title_fullStr |
Image forgery detection a survey of recent deep-learning approaches |
| title_full_unstemmed |
Image forgery detection a survey of recent deep-learning approaches |
| title_sort |
image forgery detection a survey of recent deep-learning approaches |
| publisher |
Springer |
| publishDate |
2023 |
| url |
https://link.springer.com/article/10.1007/s11042-022-13797-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8330 |
| _version_ |
1764268034708471808 |
| score |
8.881002 |