Name
Abstract | ||
|---|---|---|
The detection of weakly hidden information in a JPEG compressed image is challenging. In this paper, we propose a 32-layer convolutional neural network (CNN) involving feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient, and the sharing of features and bottleneck layers can also dramatically reduce the number of parameters in the proposed CNN model. To further improve the detection accuracy and combine the directional features from the selection-channel-aware Gabor filtering residual (SCA-GFR) method with Gabor filtering and non-directional feature maps from the CNN model, an ensemble architecture called CNN-SCA-GFR is used, which combines the proposed CNN method with the conventional SCA-GFR method to detect J-UNIWARD and UERD. This can significantly reduce the detection error rate to below that of the existing JPEG steganalysis methods. For example, in the detection of J-UNIWARD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.67% lower than that achieved by XuNet, and 7.89% lower than that achieved by the conventional SCA-GFR method. When detecting UERD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.94% lower than that achieved by XuNet, and 10.28% lower than that achieved by the conventional SCA-GFR method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-018-6878-4 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Adaptive steganography, JPEG image steganalysis, Convolutional neural networks, Ensemble | Residual,Computer vision,Bottleneck,Pattern recognition,Computer science,Convolutional neural network,Word error rate,Filter (signal processing),JPEG,Artificial intelligence,Concatenation,Steganalysis | Journal |
Volume | Issue | ISSN |
78 | 7 | 1573-7721 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhua Yang | 1 | 52 | 7.10 |
| Xiangui Kang | 2 | 424 | 37.76 |
| Edward K. Wong | 3 | 268 | 24.19 |
| Yun Q. Shi | 4 | 2918 | 199.53 |