Detecting fingerprints of audio steganography software
Chen Gong, Jinghong Zhang, Yunzhao Yang, Xiaowei Yi, Xianfeng Zhao, Yi Ma · Forensic Science International Reports · 2020
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.