AAC Audio Compression Detection Based on QMDCT Coefficient

Huang, Qijuan; Wang, Rangding; Yan, Diqun; Zhang, Jian

doi:10.1007/978-3-030-00021-9_32

Cited by 6 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was reported that the proposed features gave higher performance when the first compression bit rate (BR1) was lower than that of the second compression bit rate (BR2). In [27], QMDCT coefficients were used with the SVM classifier to detect DC AAC signals. It was found that QMDCT coefficients provided reasonable performance when the BR2 value was higher than BR1.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection

Büker,

Hanilçi

2024

Applied Sciences

View full text Add to dashboard Cite

Determining whether an audio signal is single compressed (SC) or double compressed (DC) is a crucial task in audio forensics, as it is closely linked to the integrity of the recording. In this paper, we propose the utilization of phase spectrum-based features for detecting DC narrowband and wideband adaptive multi-rate (AMR-NB and AMR-WB) speech. To the best of our knowledge, phase spectrum features have not been previously explored for DC audio detection. In addition to introducing phase spectrum features, we propose a novel parallel LSTM system that simultaneously learns the most representative features from both the magnitude and phase spectrum of the speech signal and integrates both sets of information to further enhance its performance. Analyses demonstrate significant differences between the phase spectra of SC and DC speech signals, suggesting their potential as representative features for DC AMR speech detection. The proposed phase spectrum features are found to perform as well as magnitude spectrum features for the AMR-NB codec, while outperforming the magnitude spectrum in detecting AMR-WB speech. The proposed phase spectrum features yield 8% performance improvement in terms of true positive rate over the magnitude spectrogram features. The proposed parallel LSTM system further improves DC AMR-WB speech detection.

show abstract

Section: Related Workmentioning

confidence: 99%

Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection

Büker,

Hanilçi

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In [214], the authors used the Huffman table indices in AAC files for double compression detection. Huang et al achieved AAC double compression detection using the Quantized MDCT (QMDCT) coefficients [215]. In [216], the authors used the scalefactors from AAC compressed signals for double compression detection.…”

Section: Metadata Forensicsmentioning

confidence: 99%

An Overview of Recent Work in Media Forensics: Methods and Threats

Bhagtani¹,

Yadav²,

Bartusiak³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, we review recent work in media forensics for digital images, video, audio (specifically speech), and documents. For each data modality, we discuss synthesis and manipulation techniques that can be used to create and modify digital media. We then review technological advancements for detecting and quantifying such manipulations. Finally, we consider open issues and suggest directions for future research.

show abstract

Double Compressed Wideband AMR Speech Detection Using Deep Neural Networks

Büker,

Hanilçi

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

AAC Audio Compression Detection Based on QMDCT Coefficient

Cited by 6 publications

References 12 publications

Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection

Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection

An Overview of Recent Work in Media Forensics: Methods and Threats

Double Compressed Wideband AMR Speech Detection Using Deep Neural Networks

Contact Info

Product

Resources

About