Deep Learning with Feature Reuse for JPEG Image Steganalysis
| Autor: | Yun Q. Shi, Jianhua Yang, Xiangui Kang, Edward K. Wong |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Steganalysis
021110 strategic defence & security studies Computer science business.industry Deep learning 0211 other engineering and technologies Word error rate Pattern recognition 02 engineering and technology computer.file_format Convolutional neural network JPEG Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Discrete cosine transform 020201 artificial intelligence & image processing Artificial intelligence business computer Transform coding |
| Zdroj: | APSIPA |
| DOI: | 10.23919/apsipa.2018.8659589 |
| Popis: | It is challenging to detect weak hidden information in a JPEG compressed image. In this paper, we propose a 32-layer convolutional neural networks (CNNs) with feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient and information, and the shared features and bottleneck layers in the proposed CNN model further reduce the number of parameters dramatically. The experimental results shown that the proposed method significantly reduce the detection error rate compared with the existing JPEG steganalysis methods, e.g. state-of-the-art XuNet method and the conventional SCA-GFR method. Compared with XuNet method and conventional method SCA-GFR in detecting J-UNIWARD at 0.1 bpnzAC (bit per non-zero AC DCT coefficient), the proposed method can reduce detection error rate by 4.33% and 6.55% respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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