Abstract
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.
Highlights
Adaptive multirate (AMR) audio codec [1] is one of the most popular audio codec standards, which is optimized for speech signals and encodes narrowband (200–3400 Hz) signals, with sampling frequency of 8000 Hz [2]
As more and more AMR audio appears as evidence in forensics scene, it is important to help the investigators to address the weakness of AMR compression detectors. erefore, in this paper, we propose an antiforensic method utilizing a generative adversarial network (GAN) framework which comprised of two networks: a generator and a discriminator. e generated data can statistically model the distribution of real data [19]
We have proposed a new method to prove the weakness of the forensic detectors of AMR compression
Summary
AMR audio codec [1] is one of the most popular audio codec standards, which is optimized for speech signals and encodes narrowband (200–3400 Hz) signals, with sampling frequency of 8000 Hz [2]. To manipulate an AMR audio, attacker should decompress it into raw waveform first and do the forgery operations and decompress it into AMR format. Many forensic techniques have been proposed to detect compression history of AMR audios based on traditional methods [3,4,5] and deep learning methods [2, 6, 7]. To represent the difference of single compressed audios and double compressed audios, traditional AMR compression detection techniques rely on lowlevel acoustic features such as sub-band energy and linear prediction coefficients (LPCs), which acquire professional acoustic knowledge. Deep learning methods are gaining popularity in forensic research studies, which can capture the highly complex feature from a raw sample by training large-scale sample data with a neural network
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