Name
Abstract | ||
|---|---|---|
In realistic steganalysis applications, the mismatched problem can lead to the degradation of performance in steganalysis. The main reason is the discrepancy of feature distributions between training set and testing set. In this paper, we present a Contribution-based Feature Transfer (CFT) algorithm for JPEG mismatched steganalysis. CFT tries to learn two transformations to transfer training set features by evaluating both the sample feature and dimensional feature contributions. We can obtain new feature representations so as to approach the feature distribution of the testing samples. The comparison to prior arts reveals the superiority of CFT on the experiments for the mismatched JPEG steganalysis in the heterogeneous cover source scenario. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Mismatched steganalysis, feature transfer, contribution, JPEG image |
Field | DocType | ISSN |
Training set,Kernel (linear algebra),Pattern recognition,Computer science,Transform coding,JPEG,Artificial intelligence,Steganalysis | Conference | 1522-4880 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaoyu Feng | 1 | 6 | 0.75 |
| Xiangwei Kong | 2 | 387 | 37.93 |
| Ming Li | 3 | 388 | 37.81 |
| Yong Yang | 4 | 3 | 1.05 |
| Yanqing Guo | 5 | 35 | 6.24 |