Detection of double JPEG compression using modified DenseNet model
| Autor: | Xinpeng Zhang, Guorui Feng, Ximei Zeng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science 020207 software engineering 02 engineering and technology computer.file_format JPEG Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Jpeg compression Computer vision Artificial intelligence business computer Software |
| Zdroj: | Multimedia Tools and Applications. 78:8183-8196 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-018-6737-3 |
| Popis: | With the increasing tendency of the tempering of JPEG images, development of methods detecting image forgery is of great importance. In many cases, JPEG image forgery is usually accompanied with double JPEG compression, leaving no visual traces. In this paper, a modified version of DenseNet (densely connected convolutional networks) is proposed to accomplish the detection task of primary JPEG compression among double compressed images. A special filtering layer in the front of the network contains typically selected filtering kernels that can help the network following to discriminating the images more easily. As shown in the results, the network has achieved great improvement compared to the-state-of-the-art method especially on the classification accuracy among images with lower quality factors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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