Name
Abstract | ||
|---|---|---|
Source camera identification is still a hard task in forensics community, especially for the case of the small query image size. In this paper, we propose a solution to identify the source camera of the small-size images: content-adaptive fusion network. In order to learn better feature representation from the input data, content-adaptive convolutional neural networks(CA-CNN) are constructed. We add a convolutional layer in preprocessing stage. Moreover, with the purpose of capturing more comprehensive information, we parallel three CA-CNNs: CA3-CNN, CA5-CNN, CA7-CNN to get the content-adaptive fusion network. The difference of three CA-CNNs lies in the convolutional kernel size of pre-processing layer. The experimental results show that the proposed method is practicable and satisfactory. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Kernel (linear algebra),Content adaptive,Computer vision,Pattern recognition,Camera identification,Convolutional neural network,Computer science,Fusion,Preprocessor,Artificial intelligence,Image resolution,Machine learning |
DocType | Volume | Citations |
Journal | abs/1703.04856 | 1 |
PageRank | References | Authors |
0.37 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengpeng Yang | 1 | 13 | 3.64 |
| Wei Zhao | 2 | 81 | 19.49 |
| Rongrong Ni | 3 | 718 | 53.52 |
| Yao Zhao | 4 | 1926 | 219.11 |