Non-Degraded Adaptive HEVC Steganography by Advanced Motion Vector Prediction
| Autor: | Shuowei Liu, Xianfeng Zhao, Yongjian Hu, Liu Beibei |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Steganalysis
Steganography Computer science business.industry Applied Mathematics Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Video quality Motion vector Feature (computer vision) Encoding (memory) Signal Processing Artificial intelligence Electrical and Electronic Engineering business Decoding methods |
| Zdroj: | IEEE Signal Processing Letters. 28:1843-1847 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2021.3111565 |
| Popis: | Current video steganography operates with either the decoded frame images or the compression coding parameters, which could cause quality degradation of the reconstructed frames. In this letter, by exploiting the advanced motion vector prediction (AMVP) technique of High Efficiency Video Coding (HEVC) standard, we propose a non-degraded adaptive steganographic approach for H.265/HEVC videos. The index value in the candidate list of the prediction unit (PU) is used for embedding. Experimental results demonstrate the superiority of the proposed steganographic approach against both hand-crafted feature-based and deep learning network-based steganalytic detectors. Our work explores a new embedding space that is not previously studied. It is a significant development in finding new ways to escape from video quality change-based steganalysis. |
| Databáze: | OpenAIRE |
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