Penyisipan Citra pada Audio dengan Kode PN Terdistribusi Gaussian

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

High Payload SVD based Audio Watermarking Scheme Inherently Robust to mp3 Compression

SVD based watermarking schemes met the requirement of imperceptibility, having moderate payload and are robust to most common signal processing attacks. But, when the watermarked audios are compressed at higher compression i.e. compression at lower bit rate, the percentage retrieval of the watermark is reduced. Since the audios posted on the networked environment i.e. world wide web (www) are mostly mp3 audios compressed at different compression rate, there is a need to develop a watermarking scheme which is inherently robust to compression attack at different bit rate. Scheme presented in this paper is having a high payload and is inherently robust to compression attack. In the proposed scheme only the blocks that resist the different watermarking attacks along with mp3 compression at different bit rates are selected for watermark embedding. A preliminary analysis of the mp3 attack on the individual blocks is done prior to finally embedding the watermark for making a watermarking scheme robust to mp3 compression and other attacks.

Compressive Sensing Audio Watermarking dengan Metode LWT dan QIM

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.