Image forgery detection algorithm based on U-shaped detection network
Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stag...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-04-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019086/ |
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| _version_ | 1841539323603714048 |
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| author | Zhuzhu WANG |
| author_facet | Zhuzhu WANG |
| author_sort | Zhuzhu WANG |
| collection | DOAJ |
| description | Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness. |
| format | Article |
| id | doaj-art-08155ec1afe94bcf8a9fa16f73851a5f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-08155ec1afe94bcf8a9fa16f73851a5f2025-01-14T07:16:47ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-04-014017117859726615Image forgery detection algorithm based on U-shaped detection networkZhuzhu WANGAiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019086/U-shaped detection networkhidden feature informationconditional random fieldimage forgery detection |
| spellingShingle | Zhuzhu WANG Image forgery detection algorithm based on U-shaped detection network Tongxin xuebao U-shaped detection network hidden feature information conditional random field image forgery detection |
| title | Image forgery detection algorithm based on U-shaped detection network |
| title_full | Image forgery detection algorithm based on U-shaped detection network |
| title_fullStr | Image forgery detection algorithm based on U-shaped detection network |
| title_full_unstemmed | Image forgery detection algorithm based on U-shaped detection network |
| title_short | Image forgery detection algorithm based on U-shaped detection network |
| title_sort | image forgery detection algorithm based on u shaped detection network |
| topic | U-shaped detection network hidden feature information conditional random field image forgery detection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019086/ |
| work_keys_str_mv | AT zhuzhuwang imageforgerydetectionalgorithmbasedonushapeddetectionnetwork |