利用可逆网络的音频藏图算法

doi:10.19665/j.issn1001-2400.20240303

Abstract

Abstract:

Invertible Neural Networks(INNs) are well suited for the field of information hiding due to the fact that their inherent reversible structure.Images are able to efficiently convey information in a vivid and hierarchical manner,while audio is a widely used and distributed media file with a large embedding capacity.Therefore,hiding images in audio is of high research and application value.In the task of hiding images in audio,how to represent audio and image data and how to improve the quality of reconstructed images while reducing audio distortion are two important issues.To address these two problems,this paper proposes an algorithm based on INNs to hide images in audio.Inspired by the data processing methods in JPEG image compression,an image feature extraction and representation module is proposed for data feature representation.This module performs block-wise discrete cosine transform,Zigzag scanning,and high-low frequency separation operation on color images,extracting the frequency domain features of the image and obtaining its one-dimensional representation.In addition,in order to reduce audio distortion and improve the quality of reconstructed images,this paper uses the wavelet transform to separate the high and low frequency components of audio and introduces INNs to embed the secret image into the high-frequency region of the cover audio.Experimental results show that the proposed algorithm can generate higher quality steganographic audio and reconstruct more restored color images while achieving a high embedding rate,and that the proposed algorithm exhibits good security.

Key words: steganography, image hiding, invertible neural networks, wavelet transforms, discrete cosine transforms

CLC Number:

TP309.7

ZHANG Xiaohong, XIANG Shijun, HUANG Hongbin. Hiding images in audio based on invertible neural networks[J].Journal of Xidian University, 2024, 51(4): 226-238.

Figures/Tables 11

References 36

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网络结构	操作	输出尺寸
输入特征		c_in×32 768
卷积单元1	Conv1d+PReLU	32×16 384
卷积单元2	Conv1d+PReLU	64×8 192
卷积单元3	Conv1d+PReLU	128×4 096
卷积单元4	Conv1d+PReLU	256×2 048
转置卷积单元1	deConv1d+PReLU拼接卷积单元3的输出	128×4 096
转置卷积单元2	deConv1d+PReLU拼接卷积单元2的输出	64×8 192
转置卷积单元3	deConv1d+PReLU拼接卷积单元1的输出	32×16 384
转置卷积单元4	deConv1d	c_out×32 768

方法	嵌入容量/(Kb·s^-1)
ISGAN^[11]	529.2
PixInWav^[12]	1 027.3
文中方法	1 058.4

方法	隐写音频			重构图像
方法	SNR/dB	MSE	MOS	PSNR/dB	SSIM	MOS
ISGAN^[11]	-0.08	3.73E-4	1.58	17.69	0.6913	3.29
PixInWav^[12]	17.34	1.32E-5	3.42	27.73	0.9019	4.17
文中sw/o dct	31.30	4.86E-7	4.36	11.48	0.3204	1.00
文中sw/o dwt	19.72	1.27E-5	4.06	23.14	0.6351	2.94
文中方法	20.30	1.23E-5	4.13	30.99	0.9065	4.48

方法	检测准确率/%
ISGAN^[11]	97.56
PixInWav^[12]	82.13
文中方法	59.88

Hiding images in audio based on invertible neural networks

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