Improved quantization index modulation watermarking robust against amplitude scaling distortions

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.

Improving security of quantization-index-modulation steganography in low bit-rate speech streams

Multimedia Systems ◽

10.1007/s00530-013-0302-8 ◽

2013 ◽

Vol 20 (2) ◽

pp. 143-154 ◽

Cited By ~ 27

Author(s):

Hui Tian ◽

Jin Liu ◽

Songbin Li

Keyword(s):

Bit Rate ◽

Low Bit Rate ◽

Comparison of Quantization Index Modulation and Forbidden Zone Data Hiding for compressed domain video data hiding

2009 IEEE 17th Signal Processing and Communications Applications Conference ◽

10.1109/siu.2009.5136418 ◽

2009 ◽

Cited By ~ 2

Author(s):

Ersin Esen ◽

Zafer Dogan ◽

Tugrul K. Ates ◽

A. Aydin Alatan

Keyword(s):

Data Hiding ◽

Video Data ◽

Compressed Domain ◽

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering ◽

A New Watermarking Algorithm for Point Model Using Angle Quantization Index Modulation

10.2991/nceece-15.2016.173 ◽

2016 ◽

Cited By ~ 1

Author(s):

Xiaoqing Feng

Keyword(s):

Point Model ◽

Blind Watermarking Based on Adaptive Lattice Quantization Index Modulation

Digital-Forensics and Watermarking - Lecture Notes in Computer Science ◽

10.1007/978-3-319-31960-5_20 ◽

2016 ◽

pp. 239-249 ◽

Cited By ~ 2

Author(s):

Bingwen Feng ◽

Wei Lu ◽

Wei Sun ◽

Zhuoqian Liang ◽

Juan Liu

Keyword(s):

Blind Watermarking ◽

Detection and Removal of Hidden Data in Images Embedded with Quantization Index Modulation

Lecture Notes in Computer Science - Computer Network Security ◽

10.1007/978-3-540-45215-7_31 ◽

2003 ◽

pp. 360-370 ◽

Cited By ~ 2

Author(s):

Kaiwen Zhang ◽

Shuozhong Wang ◽

Xinpeng Zhang

Keyword(s):

Hidden Data ◽

The Importance of Aliasing in Structured Quantization Index Modulation Data Hiding

Digital Watermarking - Lecture Notes in Computer Science ◽

10.1007/978-3-540-24624-4_1 ◽

2004 ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Fernando Pérez-González

Keyword(s):

Data Hiding ◽

Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec

Future Internet ◽

10.3390/fi12010017 ◽

2020 ◽

Vol 12 (1) ◽

pp. 17

Author(s):

Zhijun Wu ◽

Rong Li ◽

Panpan Yin ◽

Changliang Li

Keyword(s):

Transition Probability ◽

Principal Component ◽

Transition Probability Matrix ◽

Support Vector ◽

Distribution Probability ◽

Before And After ◽

Security Of Network ◽

Index Value ◽

Steganalysis is used for preventing the illegal use of steganography to ensure the security of network communication through detecting whether or not secret information is hidden in the carrier. This paper presents an approach to detect the quantization index modulation (QIM) of steganography in G.723.1 based on the analysis of the probability of occurrence of index values before and after steganography and studying the influence of adjacent index values in voice over internet protocol (VoIP). According to the change of index value distribution characteristics, this approach extracts the distribution probability matrix and the transition probability matrix as feature vectors, and uses principal component analysis (PCA) to reduce the dimensionality. Through a large amount of sample training, the support vector machine (SVM) is designed as a classifier to detect the QIM steganography. The speech samples with different embedding rates and different durations were tested to verify their impact on the accuracy of the steganalysis. The experimental results show that the proposed approach improves the accuracy and reliability of the steganalysis.