Detecting fingerprints of audio steganography software

Chen Gong; Jinghong Zhang; Yunzhao Yang; Xiaowei Yi; Xianfeng Zhao; Yi Ma

Forensic Science International: Reports (Dec 2020)

Detecting fingerprints of audio steganography software

Chen Gong,
Jinghong Zhang,
Yunzhao Yang,
Xiaowei Yi,
Xianfeng Zhao,
Yi Ma

Affiliations

Chen Gong: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China; Corresponding author at: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
Jinghong Zhang: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China
Yunzhao Yang: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China
Xiaowei Yi: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China
Xianfeng Zhao: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China
Yi Ma: Beijing Information Technology Institute, Beijing 100094, China

Journal volume & issue: Vol. 2
p. 100075

Abstract

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Steganography has experienced rapidly growth with spreading access to the Internet in recent decades. Such low cost, simple process and easy promotion steganographic softwares pose serious and growing threats and challenges to network security. The fingerprint of steganography has proven itself an effective solution. However, large gaps exist between experiments results and practical applications. Few attention has been paid on the fingerprint of steganography. In this paper, we provide highly effective solutions to identify the fingerprints of audio steganography. Based on the analysis of audio stego produced by three kinds of audio steganographic softwares, including Xiao Steganography, Invisible Secrets, Deep Sound, we successfully detect audio stego across WAV files. Besides, we summarize a general approach of fingerprint extraction, and its effectiveness has been confirmed through experiments. The results solve an urgent need for further research on reverse engineering steganography softwares and detecting audio stego.

Published in Forensic Science International: Reports

ISSN: 2665-9107 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Law: Law in general. Comparative and uniform law. Jurisprudence: Comparative law. International uniform law: Criminal law and procedure
Website: https://www.journals.elsevier.com/forensic-science-international-reports

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