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Deep neural networks based invisible steganography for audio-into-image algorithm

Huu, Q.P. and Dinh, T.H. and Tran, N.N. and Van, T.P. and Minh, T.T. (2019) Deep neural networks based invisible steganography for audio-into-image algorithm. In: 8th IEEE Global Conference on Consumer Electronics, GCCE 2019, 15 October 2019 through 18 October 2019.

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Abstract

In the last few years, steganography has attracted increasing attention from a large number of researchers since its applications are expanding further than just the field of information security. The most traditional method is based on digital signal processing (DSP), such as least significant bit (LSB) encoding. Recently, there have been some new approaches employing deep learning to address the problem of steganography. However, most of the existing approaches are designed for image-in-image steganography. In this paper, the use of deep learning techniques to hide secret audio into the digital images is proposed. We employ a joint deep neural network architecture consisting of two sub-models: the first network hides the secret audio into an image, and the second one is responsible for decoding the image to obtain the original audio. Extensive experiments are conducted with a set of 24K images and the VIVOS Corpus audio dataset11https://ailab.hcmus.edu.vn/downloads, Through experimental results, it can be seen that our method is more effective than traditional approaches. The integrity of both image and audio is well preserved, while the maximum length of the hidden audio is significantly improved. © 2019 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/GCCE46687.2019.9015498
Uncontrolled Keywords: Convolutional neural networks; Deep learning; Digital signal processing; Image enhancement; Network architecture; Network security; Security of data; Steganography; Deep Convolutional Neural Network (DCNN); Digital signal processing (DSP); Image steganography; ITS applications; Learning techniques; Least significant bits; Secure data; Traditional approaches; Deep neural networks
Additional Information: Conference code: 158116. Language of original document: English. All Open Access, Green.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9233

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