Passive forensic based on spatio-temporal localization of video object removal tampering
To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extr...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020151/ |
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| _version_ | 1841539301933842432 |
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| author | Linqiang CHEN Quanxin YANG Lifeng YUAN Ye YAO Zhen ZHANG Guohua WU |
| author_facet | Linqiang CHEN Quanxin YANG Lifeng YUAN Ye YAO Zhen ZHANG Guohua WU |
| author_sort | Linqiang CHEN |
| collection | DOAJ |
| description | To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain. |
| format | Article |
| id | doaj-art-b31b5b5921f448e69e944b0cac0292c3 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b31b5b5921f448e69e944b0cac0292c32025-01-14T07:19:41ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-07-014111012059736685Passive forensic based on spatio-temporal localization of video object removal tamperingLinqiang CHENQuanxin YANGLifeng YUANYe YAOZhen ZHANGGuohua WUTo address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020151/video object removal tamperingspatio-temporal localizationvideo passive forensicobject detection based on 3D convolution |
| spellingShingle | Linqiang CHEN Quanxin YANG Lifeng YUAN Ye YAO Zhen ZHANG Guohua WU Passive forensic based on spatio-temporal localization of video object removal tampering Tongxin xuebao video object removal tampering spatio-temporal localization video passive forensic object detection based on 3D convolution |
| title | Passive forensic based on spatio-temporal localization of video object removal tampering |
| title_full | Passive forensic based on spatio-temporal localization of video object removal tampering |
| title_fullStr | Passive forensic based on spatio-temporal localization of video object removal tampering |
| title_full_unstemmed | Passive forensic based on spatio-temporal localization of video object removal tampering |
| title_short | Passive forensic based on spatio-temporal localization of video object removal tampering |
| title_sort | passive forensic based on spatio temporal localization of video object removal tampering |
| topic | video object removal tampering spatio-temporal localization video passive forensic object detection based on 3D convolution |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020151/ |
| work_keys_str_mv | AT linqiangchen passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering AT quanxinyang passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering AT lifengyuan passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering AT yeyao passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering AT zhenzhang passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering AT guohuawu passiveforensicbasedonspatiotemporallocalizationofvideoobjectremovaltampering |