Efficient Multi-Scale Feature Fusion for Image Manipulation Detection
Convolutional Neural Network (CNN) has made extraordinary progress in image classification tasks. However, it is less effective to use CNN directly to detect image manipulation. To address this problem, we propose an image filtering layer and a multi-scale feature fusion module which can guide the m...
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| Veröffentlicht in: | IEICE Transactions on Information and Systems 2022/05/01, Vol.E105.D(5), pp.1107-1111 |
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| container_title | IEICE Transactions on Information and Systems |
| container_volume | E105.D |
| creator | ZHANG, Yuxue FENG, Guorui |
| description | Convolutional Neural Network (CNN) has made extraordinary progress in image classification tasks. However, it is less effective to use CNN directly to detect image manipulation. To address this problem, we propose an image filtering layer and a multi-scale feature fusion module which can guide the model more accurately and effectively to perform image manipulation detection. Through a series of experiments, it is shown that our model achieves improvements on image manipulation detection compared with the previous researches. |
| doi_str_mv | 10.1587/transinf.2021EDL8099 |
| format | Article |
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| issn | 0916-8532 1745-1361 |
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| source | J-STAGE Free; EZB-FREE-00999 freely available EZB journals |
| subjects | Artificial neural networks convolutional neural network high-pass filter Image classification Image filters Image manipulation image manipulation detection multi-scale feature fusion |
| title | Efficient Multi-Scale Feature Fusion for Image Manipulation Detection |
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