Coverless image steganography framework using distance local binary pattern and convolutional neural network

Luis A. Sandoval-Bravo; Volodymyr I. Ponomaryov; Rogelio Reyes-Reyes; Clara Cruz-Ramos

doi:10.1117/12.2556310

22 April 2020 Coverless image steganography framework using distance local binary pattern and convolutional neural network

Luis A. Sandoval-Bravo, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos

Author Affiliations +

Proceedings Volume 11401, Real-Time Image Processing and Deep Learning 2020; 114010D (2020) https://doi.org/10.1117/12.2556310
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Steganography in digital images commonly uses a carrier image and embeds secret data into to create the stego-image by spatial or frequency domain methods, which directly modifies the bits of the carrier image, altering the intensity of the pixels and leaving traces of modification caused by the embedding of data in the carrier image, which makes successful steganalysis possible. This paper proposes a digital image steganography framework without embedding data directly into the images that extracts the secret-data from the convolutional neural network trained with the distance local binary pattern images from an indexed image database. Experimental results demonstrate that the proposed framework is resistant to common steganalysis tools, intentional and unintentional image attacks such as luminance and contrast changes, rescaling, noise addition and compression.

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Citation Download Citation

Luis A. Sandoval-Bravo, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, and Clara Cruz-Ramos "Coverless image steganography framework using distance local binary pattern and convolutional neural network", Proc. SPIE 11401, Real-Time Image Processing and Deep Learning 2020, 114010D (22 April 2020); https://doi.org/10.1117/12.2556310

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