Quantization index modulation: a class of provably good methods for digital watermarking and information embedding

2001 ◽  
Vol 47 (4) ◽  
pp. 1423-1443 ◽  
Author(s):  
B. Chen ◽  
G.W. Wornell
Keyword(s):  
Digital Watermarking ◽  
Quantization Index Modulation ◽  
Information Embedding ◽  
Index Modulation

scholarly journals Speech Watermarking for Tampering Detection Based on Modifications to LSFs

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Xiangning Chen ◽  
Weitao Yuan ◽  
Shengbei Wang ◽  
Chao Wang ◽  
Lin Wang
Keyword(s):  
Auditory System ◽  
Digital Watermarking ◽  
Linear Prediction ◽  
Speech Signals ◽  
Quantization Index Modulation ◽  
Tampering Detection ◽  
Speech Watermarking ◽  
Dither Modulation ◽  
Detection Approach ◽  
Index Modulation

There have been serious issues concerning the protection of speech signals from malicious tampering. Digital watermarking has been paid much attention in solving this problem. This paper proposes a tampering detection approach based on speech watermarking by modifying the line spectral frequencies (LSFs). Watermarks are embedded into LSFs that derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Minor modifications to LSFs introduced by quantization not only enable the watermarks to be inaudible to human auditory system but also provide the possibility of robustness against meaningful processing and fragility against tampering. We evaluated the proposed approach with objective evaluations with respect to inaudibility, robustness, and fragility. The results indicated that the proposed approach for tampering detection not only satisfied inaudibility but also provided good robustness against meaningful processing and fragility against malicious tampering.

scholarly journals Watermarking algorithms for JPEG 2000 lossy compressed images

2019 ◽  
pp. 366-370 ◽  
Author(s):  
V Fedoseev ◽  
T Androsova
Keyword(s):  
Image Quality ◽  
Experimental Investigations ◽  
Quantization Index Modulation ◽  
Jpeg 2000 ◽  
Quantization Parameter ◽  
Hidden Information ◽  
High Quality ◽  
Compressed Images ◽  
Information Embedding ◽  
Index Modulation

In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a property of semi-fragility to the image quality. It means that the hidden information is preserved after high-quality compression, and is destroyed in the case of significant degradation. Experimental investigations confirm this property for both algorithms. They also show that the introduced embedding distortions in terms of PSNR and PSNR-HVS are in almost linear dependence on the quantization parameter. It allows us to control the quality at an acceptable level when information embedding

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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