2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-30.6
Paper Title Hide Chopin in the Music: Efficient Information Steganography via Random Shuffling
Authors Zhun Sun, BIGO Technology Pte. Ltd., Singapore; Chao Li, Qibin Zhao, RIKEN, Japan
SessionIVMSP-30: Inverse Problems in Image & Video Processing
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Information steganography is a family of techniques that hide secret messages into a carrier; thus, the messages can only be extracted by receivers with a correct key. Although many approaches have been proposed to achieve this purpose, historically, it is a difficult problem to conceal a large amount of information without occasioning human perceptible changes. In this paper, we explore the room introduced by the low-rank property of natural signals (i.e., images, audios) and propose a training-free model for efficient information steganography, which provides a capacity of hiding full-size images into carriers of the same spatial resolution. The key of our method is to randomly shuffle the secrets and carry out a simple reduction summation with the carrier. On the other hand, the secret images can be reconstructed by solving a convex optimization problem similar to the ordinary tensor decomposition. In the experimental analysis, we carry out two tasks: concealing a full-RGB-color image into a gray-scale image; concealing images into music signals. The results confirm the ability of our model to handle massive secret payloads. The code of our paper is provided in https://github.com/minogame/icassp-SIC.