Journal
ELECTRONICS
Volume 11, Issue 24, Pages -Publisher
MDPI
DOI: 10.3390/electronics11244102
Keywords
steganography; deep learning; generative adversarial network; robustness
Categories
Funding
- Scientific Research Common Program of Beijing Municipal Commission of Education
- [KM202110015004]
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An end-to-end video steganography based on GAN and multi-scale deep learning network is proposed in this paper. By introducing a noise layer, the model is able to resist video compressions and has achieved good experimental results.
The purpose of video steganography is to hide messages in the video file and prevent them from being detected, and finally the secret message can be extracted completely at the receiver. In this paper, an end-to-end video steganography based on GAN and multi-scale deep learning network is proposed, which consists of the encoder, decoder and discriminator. However, in the transmission process, videos will inevitably be encoded. Thus, a noise layer is introduced between the encoder and the decoder, which makes the model able to resist popular video compressions. Experimental results show that the proposed end-to-end steganography has achieved high visual quality, large embedding capacity, and strong robustness. Moreover, the proposed method performances better compared to the latest end-to-end video steganography.
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