StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module
| Autor: | Wenxin Wang, Zimei Xie, Chuan Qin, Xintao Duan, Dongli Yue, Nao Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Pooling Feature extraction 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Convolutional neural network Distortion Pyramid 0202 electrical engineering electronic engineering information engineering General Materials Science Pyramid (image processing) Image steganography pyramid pooling module 021110 strategic defence & security studies Steganography business.industry Deep learning Payload (computing) General Engineering deep neural network Pattern recognition 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 195253-195262 (2020) |
| ISSN: | 2169-3536 |
| Popis: | In terms of payload capacity and visual effects, the existing image steganography technology based on deep neural networks still needs improvement, to solve this problem, this article proposes a new deep convolutional steganography network based on the pyramid pooling module to achieve better image steganography. The deep convolutional neural network itself can extract features efficiently. Based on the combination of up-sampling structure, we added a pyramid pool module, under the premise of ensuring safety, fully integrated the previous important global features, achieved good hiding and extraction effects, fully integrated the previous important global features, and effective it reduces the loss of contextual information between different sub-regions in the feature extraction process and achieves better hiding and extraction effects under the premise of ensuring security. Experiments show that the average peak signal-to-noise ratio (PSNR)/structure similarity (SSIM) and other indicators between the images obtained by this method have achieved good results in the experiment. Also, we have verified through ablation experiments that the pyramid pooling module can enhance the steganography effect of the network model and can further cut down the loss function of the model. |
| Databáze: | OpenAIRE |
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