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2 results on '"stdct"'

1. Enhancing steganography for hiding pixels inside audio signals

Author

Universitat Politècnica de Catalunya, Dublin City University, Ros Alonso, Jaume, Universitat Politècnica de Catalunya, Dublin City University, and Ros Alonso, Jaume

Abstract

Multimodal steganography consists of concealing a signal into another one of a different medium, such that the latter is only very slightly distorted and the hidden information can be later recovered. A previous work employed deep learning techniques to this end by hiding an image inside an audio signal's spectrogram in a way that the encoding of one is independent of the other. In this work we explore the way in which images were being encoded previously and present a collection of improvements that produce a significant increase in the quality of the system. These mainly consist in encoding the image in a smarter way such that more information is able to be transmitted in a container of the same size. We also explore the possibility of using the short-time Fourier transform phase as an alternative to the magnitude and to randomly permute the signal to break the structure of the noise. Finally, we report results when using a larger container signal and outline possible directions for future work.

Published
2022

2. Enhancing steganography for hiding pixels inside audio signals

Author

Ros Alonso, Jaume, Universitat Politècnica de Catalunya, and Dublin City University

Subjects
aprenetatge, esteganografia, amagat, amagar, hide, hiding, residual, neuronal, imatge, stdct, pixinwav, audio, image, neural, steganography, xarxa, learning, multimodal, profund, Criptografia, stft, deep, hidden, network, Cryptography, short time discrete cosine transform, short time fourier transform, Informàtica::Seguretat informàtica::Criptografia [Àrees temàtiques de la UPC]
Abstract

Multimodal steganography consists of concealing a signal into another one of a different medium, such that the latter is only very slightly distorted and the hidden information can be later recovered. A previous work employed deep learning techniques to this end by hiding an image inside an audio signal's spectrogram in a way that the encoding of one is independent of the other. In this work we explore the way in which images were being encoded previously and present a collection of improvements that produce a significant increase in the quality of the system. These mainly consist in encoding the image in a smarter way such that more information is able to be transmitted in a container of the same size. We also explore the possibility of using the short-time Fourier transform phase as an alternative to the magnitude and to randomly permute the signal to break the structure of the noise. Finally, we report results when using a larger container signal and outline possible directions for future work.

Published
2022

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