An Antiforensic Method against AMR Compression Detection
Joint Authors
Dong, Li
Li, Xiaowen
Wang, Rangding
Yan, Diqun
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Adaptive multirate (AMR) compression audio has been exploited as an effective forensic evidence to justify audio authenticity.
Little consideration has been given, however, to antiforensic techniques capable of fooling AMR compression forensic algorithms.
In this paper, we present an antiforensic method based on generative adversarial network (GAN) to attack AMR compression detectors.
The GAN framework is utilized to modify double AMR compressed audio to have the underlying statistics of single compressed one.
Three state-of-the-art detectors of AMR compression are selected as the targets to be attacked.
The experimental results demonstrate that the proposed method is capable of removing the forensically detectable artifacts of AMR compression under various ratios with an average successful attack rate about 94.75%, which means the modified audios generated by our well-trained generator can treat the forensic detector effectively.
Moreover, we show that the perceptual quality of the generated AMR audio is well preserved.
American Psychological Association (APA)
Yan, Diqun& Li, Xiaowen& Dong, Li& Wang, Rangding. 2020. An Antiforensic Method against AMR Compression Detection. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1208741
Modern Language Association (MLA)
Yan, Diqun…[et al.]. An Antiforensic Method against AMR Compression Detection. Security and Communication Networks No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1208741
American Medical Association (AMA)
Yan, Diqun& Li, Xiaowen& Dong, Li& Wang, Rangding. An Antiforensic Method against AMR Compression Detection. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1208741
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208741