Neural Reversible Steganography with Long Short-Term Memory
نویسندگان
چکیده
Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks reversible steganography, class steganographic methods that permits distortion caused by message embedding to be removed. The underdevelopment field steganography with can attributed perception perfect reversal seems scarcely achievable, due lack transparency and interpretability networks. Rather than employing coding module scheme, we instead apply them an analytics exploits data redundancy maximise capacity. State-of-the-art schemes for images are based primarily histogram-shifting method which often modelled pixel intensity predictor. In this paper, propose refine prior estimation from conventional linear predictor through network model. refinement some extent viewed low-level vision task (e.g., noise reduction super-resolution imaging). way, explore leading-edge neuroscience-inspired model long short-term memory brief discussion its biological plausibility. Experimental results demonstrated significant boost contributed terms prediction accuracy rate-distortion performance.
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ژورنال
عنوان ژورنال: Security and Communication Networks
سال: 2021
ISSN: ['1939-0122', '1939-0114']
DOI: https://doi.org/10.1155/2021/5580272