A dual-path network for source-robust spatial image steganalysis

Fan Nie; Chaoyang Zhu

doi:10.1117/12.2628436

21 April 2022 A dual-path network for source-robust spatial image steganalysis

Fan Nie, Chaoyang Zhu

Proceedings Volume 12175, International Conference on Network Communication and Information Security (ICNCIS 2021); 1217504 (2022) https://doi.org/10.1117/12.2628436
Event: International Conference on Network Communication and Information Security (ICNCIS 2021), 2021, Beijing, China

Abstract

As the existing literatures show, deep learning based steganalysis has achieved much better detection performance compared to traditional hand-crafted feature based methods. However, most methods based on deep learning are quite vulnerable to keep great performance under the image-source mismatch scenario, which is the inconsistency of the source of the training set and testing set. To improve this drawback, we specially design the architecture of the proposed network. Moreover, we first introduce the bilinear pooling into steganalysis to reduce the data source dependence of the model for further improving the performance. Experimental results show that the proposed work can outperform other networks in the image-source mismatch scenario.

Citation Download Citation

Fan Nie and Chaoyang Zhu "A dual-path network for source-robust spatial image steganalysis", Proc. SPIE 12175, International Conference on Network Communication and Information Security (ICNCIS 2021), 1217504 (21 April 2022); https://doi.org/10.1117/12.2628436

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