SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis
Abstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may l...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12835 |
| _version_ | 1827878204064399360 |
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| author | Wentong Fan Zhenyu Li Hao Li Yi Zhang Xiangyang Luo |
| author_facet | Wentong Fan Zhenyu Li Hao Li Yi Zhang Xiangyang Luo |
| author_sort | Wentong Fan |
| collection | DOAJ |
| description | Abstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments. |
| first_indexed | 2024-03-12T17:48:09Z |
| format | Article |
| id | doaj.art-fc325936220c4538a0a0c7024681a1b8 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-03-12T17:48:09Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-fc325936220c4538a0a0c7024681a1b82023-08-03T12:43:17ZengWileyIET Image Processing1751-96591751-96672023-08-0117102894290610.1049/ipr2.12835SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysisWentong Fan0Zhenyu Li1Hao Li2Yi Zhang3Xiangyang Luo4Zhengzhou Institute of Information Science and Technology Zhengzhou Henan Province ChinaZhengzhou Institute of Information Science and Technology Zhengzhou Henan Province ChinaZhengzhou Institute of Information Science and Technology Zhengzhou Henan Province ChinaZhengzhou Institute of Information Science and Technology Zhengzhou Henan Province ChinaZhengzhou Institute of Information Science and Technology Zhengzhou Henan Province ChinaAbstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments.https://doi.org/10.1049/ipr2.12835image forensicssteganalysissteganographystego noises |
| spellingShingle | Wentong Fan Zhenyu Li Hao Li Yi Zhang Xiangyang Luo SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis IET Image Processing image forensics steganalysis steganography stego noises |
| title | SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis |
| title_full | SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis |
| title_fullStr | SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis |
| title_full_unstemmed | SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis |
| title_short | SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis |
| title_sort | snenet an adaptive stego noise extraction network using parallel dilated convolution for jpeg image steganalysis |
| topic | image forensics steganalysis steganography stego noises |
| url | https://doi.org/10.1049/ipr2.12835 |
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