Information hiding in proposed 10.6 kbps CS-ACELP based speech codec using Quantization Index Modulation

2021 ◽  
Author(s):  
Nikunj Tahilramani ◽  
Ninad Bhatt
Keyword(s):  
Information Hiding ◽  
Quantization Index Modulation ◽  
Speech Codec ◽  
Index Modulation

scholarly journals Robust information hiding in low-resolution videos with quantization index modulation in DCT-CS domain

2017 ◽  
Vol 77 (14) ◽  
pp. 18827-18847 ◽  
Author(s):  
Huimin Zhao ◽  
Qingyun Dai ◽  
J. C. Ren ◽  
Wenguo Wei ◽  
Yinyin Xiao ◽  
...  
Keyword(s):  
Information Hiding ◽  
Quantization Index Modulation ◽  
Low Resolution ◽  
Index Modulation

Information Hiding for Audio Signals

2013 ◽  
pp. 1-18
Author(s):  
Akira Nishimura ◽  
Kazuhiro Kondo
Keyword(s):  
Data Hiding ◽  
Spread Spectrum ◽  
Information Hiding ◽  
Quantization Index Modulation ◽  
Audio Signals ◽  
General Overview ◽  
Information And Communication ◽  
Hide Data ◽  
Audio Data ◽  
Index Modulation

This chapter provides a general overview of audio data hiding. The general issues are discussed first, followed by the basic techniques used to hide data in audio signals, including bit stealing, spread spectrum methods, echo methods, and quantization index modulation. This is followed by a brief description of the recent proposals presented at the Institute of Electronics, Information, and Communication Engineers of Japan (IEICE) Multimedia Information Hiding (MIH) Technical Group Meetings.

Compressive Sensing Audio Watermarking dengan Metode LWT dan QIM

2018 ◽  
Vol 6 (3) ◽  
pp. 405
Author(s):  
IRMA SAFITRI ◽  
NUR IBRAHIM ◽  
HERLAMBANG YOGASWARA
Keyword(s):  
Wavelet Transform ◽  
Compressive Sensing ◽  
Audio Watermarking ◽  
Quantization Index Modulation ◽  
Computationally Efficient ◽  
Black And White ◽  
Additional Information ◽  
Audio Files ◽  
Index Modulation ◽  
Mp3 Compression

ABSTRAKPenelitian ini mengembangkan teknik Compressive Sensing (CS) untuk audio watermarking dengan metode Lifting Wavelet Transform (LWT) dan Quantization Index Modulation (QIM). LWT adalah salah satu teknik mendekomposisi sinyal menjadi 2 sub-band, yaitu sub-band low dan high. QIM adalah suatu metode yang efisien secara komputasi atau perhitungan watermarking dengan menggunakan informasi tambahan. Audio watermarking dilakukan menggunakan file audio dengan format *.wav berdurasi 10 detik dan menggunakan 4 genre musik, yaitu pop, classic, rock, dan metal. Watermark yang disisipkan berupa citra hitam putih dengan format *.bmp yang masing-masing berukuran 32x32 dan 64x64 pixel. Pengujian dilakukan dengan mengukur nilai SNR, ODG, BER, dan PSNR. Audio yang telah disisipkan watermark, diuji ketahanannya dengan diberikan 7 macam serangan berupa LPF, BPF, HPF, MP3 compression, noise, dan echo. Penelitian ini memiliki hasil optimal dengan nilai SNR 85,32 dB, ODG -8,34x10-11, BER 0, dan PSNR ∞.Kata kunci: Audio watermarking, QIM, LWT, Compressive Sensing. ABSTRACTThis research developed Compressive Sensing (CS) technique for audio watermarking using Wavelet Transform (LWT) and Quantization Index Modulation (QIM) methods. LWT is one technique to decompose the signal into 2 sub-bands, namely sub-band low and high. QIM is a computationally efficient method or watermarking calculation using additional information. Audio watermarking was done using audio files with *.wav format duration of 10 seconds and used 4 genres of music, namely pop, classic, rock, and metal. Watermark was inserted in the form of black and white image with *.bmp format each measuring 32x32 and 64x64 pixels. The test was done by measuring the value of SNR, ODG, BER, and PSNR. Audio that had been inserted watermark was tested its durability with given 7 kinds of attacks such as LPF, BPF, HPF, MP3 Compression, Noise, and Echo. This research had optimal result with SNR value of 85.32 dB, ODG value of -8.34x10-11, BER value of 0, and PSNR value of ∞.Keywords: Audio watermarking, QIM, LWT, Compressive Sensing.

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