An adaptive and secure audio watermarking algorithm robust to MP3 compression

2009 ◽  
Author(s):  
Bingwei Chen ◽  
Jiying Zhao
Keyword(s):  
Audio Watermarking ◽  
Mp3 Compression

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.

Audio watermarking robust against time-scale modification and MP3 compression

2008 ◽  
Vol 88 (10) ◽  
pp. 2372-2387 ◽  
Author(s):  
Shijun Xiang ◽  
Hyoung Joong Kim ◽  
Jiwu Huang
Keyword(s):  
Time Scale ◽  
Audio Watermarking ◽  
Time Scale Modification ◽  
Mp3 Compression

An Audio Watermarking Algorithm Based on Chaos

2012 ◽  
Vol 532-533 ◽  
pp. 1764-1769
Author(s):  
Hong Bin Tang ◽  
Ben Bin Liang
Keyword(s):  
Data Hiding ◽  
Chaos Theory ◽  
Audio Signal ◽  
Experimental Results ◽  
Audio Watermarking ◽  
Wavelet Coefficients ◽  
Chaotic Sequences ◽  
Low Pass ◽  
Audio Data ◽  
Mp3 Compression

This thesis proposes a new algorithm of the Chaos-based audio data hiding. The Chaos theory is introduced in design a new algorithm of the audio data hiding: with one section of audio as the watermarking, the Chaotic sequences select one part of the original audio signal as the carrier, and then embed the Chaos-encrypted audio watermarking into the carrier’s wavelet coefficients. Experimental results show that embedded watermark is imperceptibility and robust to many attacks, such as noise adding, re-sampling, low pass filtering, reverberation, MP3 compression and re-quantization and so on.

scholarly journals Penyisipan Citra pada Audio dengan Kode PN Terdistribusi Gaussian

2019 ◽  
Vol 7 (2) ◽  
pp. 209
Author(s):  
GELAR BUDIMAN ◽  
SUCI AULIA ◽  
I NYOMAN APRAZ RAMATRYANA
Keyword(s):  
Normal Distribution ◽  
Additive Noise ◽  
Gain Factor ◽  
Audio Watermarking ◽  
Dimensional Vector ◽  
Normal Filter ◽  
Binary Images ◽  
Audio Watermark ◽  
Mp3 Compression ◽  
Normally Distributed

ABSTRAKPada makalah ini, perancangan audio watermarking memanfaatkan kode PN yang terdistribusi Gaussian atau Normal dengan menggunakan citra sebagai watermark yang disisipkan pada audio. Watermark yang berupa citra biner diubah ke dalam vektor 1 dimensi, kemudian dijumlahkan dengan kode PN terdistribusi normal yang disaring dengan filter psikoakustik. Setelah itu, sinyal dikalikan dengan faktor gain α sebelum dijumlahkan dengan host audio untuk mendapatkan watermarked audio. Hasil dari simulasi menunjukkan bahwa sistem memiliki kapasitas watermark yang tinggi pada 689.06 bps, imperseptibilitas yang baik pada SNR>26 dB, dan tahan terhadap serangan LPF mulai frekuensi cut off 6 kHz keatas, serangan Additive Noise mulai 40 dB keatas, resampling pada rate 16 kHz, LSC 1% - 10%, dan kompresi MP3 untuk rate 192 kbps.Kata kunci: Audio Watermarking, Kode PN, distribusi normal, filter sikoakustik ABSTRACTIn this paper, the design of audio watermarking utilizes PN code that is Gaussian or Normal distributed by using the image as a watermark inserted in the audio. The watermark in the form of binary images is converted into a 1-dimensional vector, then summed up with a normally distributed PN code filtered by a psychoacoustic filter. After that, the signal is multiplied by α gain factor before adding it to the audio host to get the watermarked audio. The result of the simulation shows that the system has a high watermark capacity at 689.06 bps, good imperceptibility at SNR> 26 dB, and withstand LPF attacks starting from 6 kHz cut-off frequency and above, Additive Noise attacks from 40 dB up, resampling at 16 kHz , LSC 1% - 10%, and MP3 compression for 192 kbps rate.Keywords: Audio Watermarking, PN code, normal distribution, psychoacoustic filter

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