Name
Abstract | ||
|---|---|---|
AbstractThis article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network CNN-based method to accurately localize the tampered regions, which combines color filter array CFA features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4018/IJDCF.2018100110 | Periodicals |
Keywords | Field | DocType |
Color Filter Array, Copy-move Forgery detection, Convolutional Neural Networks, Deep Learning | Computer vision,Computer science,Convolutional neural network,Interpolation,Image processing,Robustness (computer science),Feature extraction,Artificial intelligence,Pixel,Deep learning,Color filter array | Journal |
Volume | Issue | ISSN |
10 | 4 | 1941-6210 |
Citations | PageRank | References |
1 | 0.34 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Liu | 1 | 1501 | 170.70 |
| Yao Zhao | 2 | 1926 | 219.11 |
| Rongrong Ni | 3 | 718 | 53.52 |
| Qi Tian | 4 | 6443 | 331.75 |