Name
Abstract | ||
|---|---|---|
Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisible hidden in a host image thus learning in a very low signal-to-noise (SNR) case. In this paper, we propose a 32- layer convolutional neural Networks (CNNs) in to improve the efficiency of preprocess and reuse the features by concatenating all features from the previous layers with the same feature- map size, thus improve the flow of information and gradient. The shared features and bottleneck layers further improve the feature propagation and reduce the CNN model parameters dramatically. Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness. To further boost the detection accuracy, an ensemble architecture called as CNN-SCA-GFR is proposed, CNN-SCA- GFR is also the first work to combine the CNN architecture and conventional method in the JPEG domain. Experiments show that it can further lower detection errors. Compared with the state-of-the-art method XuNet [1] on BOSSbase, the proposed CNN-SCA-GFR architecture can reduce detection error rate by 5.67% for 0.1 bpnzAC and by 4.41% for 0.4 bpnzAC while the number of training parameters in CNN is only 17% of what used by XuNet. It also decreases the detection errors from the conventional method SCA-GFR by 7.89% for 0.1 bpnzAC and 8.06% for 0.4 bpnzAC, respectively. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Multimedia | Lossless JPEG,Bottleneck,Computer vision,Convolutional neural network,Computer science,Word error rate,Robustness (computer science),JPEG,Artificial intelligence,Steganalysis,Deep learning |
DocType | Volume | Citations |
Journal | abs/1711.09335 | 2 |
PageRank | References | Authors |
0.38 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianhua Yang | 1 | 80 | 12.95 |
| Yun Q. Shi | 2 | 2918 | 199.53 |
| Edward K. Wong | 3 | 268 | 24.19 |
| Xiangui Kang | 4 | 424 | 37.76 |