Open Access

ARTICLE

Cover Enhancement Method for Audio Steganography Based on Universal Adversarial Perturbations with Sample Diversification

Jiangchuan Li, Peisong He^*, Jiayong Liu, Jie Luo, Qiang Xia

School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, China

* Corresponding Author: Peisong He. Email:

Computers, Materials & Continua 2023, 75(3), 4893-4915. https://doi.org/10.32604/cmc.2023.036819

Received 12 October 2022; Accepted 22 February 2023; Issue published 29 April 2023

Abstract

Steganography techniques, such as audio steganography, have been widely used in covert communication. However, the deep neural network, especially the convolutional neural network (CNN), has greatly threatened the security of audio steganography. Besides, existing adversarial attacks-based countermeasures cannot provide general perturbation, and the transferability against unknown steganography detection methods is weak. This paper proposes a cover enhancement method for audio steganography based on universal adversarial perturbations with sample diversification to address these issues. Universal adversarial perturbation is constructed by iteratively optimizing adversarial perturbation, which applies adversarial attack techniques, such as Deepfool. Moreover, the sample diversification strategy is designed to improve the transferability of adversarial perturbations in black-box attack scenarios, where two types of common audio-processing operations are considered, including noise addition and moving picture experts group audio layer III (MP3) compression. Furthermore, the perturbation ensemble method is applied to further improve the attacks’ transferability by integrating perturbations of different detection networks with heterogeneous architectures. Consequently, the single universal adversarial perturbation can enhance different cover audios against a CNN-based detection network. Extensive experiments have been conducted, and the results demonstrate that the average missed-detection probabilities of the proposed method are higher than those of the state-of-the-art methods by 7.3% and 16.6% for known and unknown detection networks, respectively. It verifies the efficiency and transferability of the proposed methods for the cover enhancement of audio steganography.

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Keywords

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