콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Local-Source Enhanced Residual Network for Steganalysis of Digital Images
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2020
- 저자명 장한얼
- 학술지명 IEEE ACCESS
- 발행처명 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 5
논문 초록 (Abstract)
Steganalysis refers to the study of identifying hidden messages in images inserted by steganography. Although detection performance is greatly improved when adopting convolutional neural networks (CNNs), they require sophisticated tricks, such as preprocessing for suppression of image content, using absolute and truncated activation functions, and utilizing domain knowledge. These tricks help networks train stably and mitigate the convergence problem of early stages in training, but they also restrict the flexibility of CNNs, which limits their performance. In this paper, we propose a local-source enhanced residual network (LSER) with end-to-end learning. The LSER is simple in its architecture but has two distinct characteristics from previous methods. First, the LSER uses residual blocks without any normalization. We find batch normalization is an unnecessary module in our framework. Second, a local-source skip connection is added to bypass features of different levels, which allows more abundant feature representation. Moreover, the LSER exhibits state-of-the-art results compared with the existing work in both spatial and JPEG domain steganalysis. Furthermore, a simple self-ensemble method further improves its performance without any side information.
