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FFT-based data hiding on audio in LWT-domain using spread spectrum technique
Budiman G.a,b, Suksmono A.B.a, Danudirdjo D.a
a Graduate School of Electronic Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
b School of Electrical Engineering, Telkom University, Jl. Telekomunikasi Terusan Buah Batu, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Kauno Technologijos Universitetas. All rights reserved.Audio watermarking is a process to hide digital data without being seen or heard by the sense of sight or hearing. Watermaking is applied to insert the copyright on digital media, such as an image file, an audio file or a video file. In this paper, we propose watermarking procedure to embed spread spectrum watermark into frequency domain of adaptive selected subband from host audio. Lifting Wavelet Transform (LWT) is used to decompose the host audio into several subbands, and then Fast Fourier Transform (FFT) transforms selected several subbands with lowest energy. The watermark image is converted into one-dimensional signal, then it is modulated by imperceptible pseudo-noise (PN) code with controlled gain. Next, the frequency domain of audio is added by modulated and imperceptible watermark prior to transforming it to time domain by Inverse FFT (IFFT) obtaining watermarked subbands. Finally, the watermarked subbands are combined with other unused subbands by inverse LWT (ILWT) becoming the perfect version of watermarked audio. The result of this method has good robustness against most attacks from stirmark benchmark experiments, good imperceptibility with Signal to Noise Ratio (SNR) more than 30 dB and payload 172.66 bps.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Audio watermarking,Fast Fourier transform,Lifting wavelet transform,Spread spectrum[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Manuscript received 4 August, 2019; accepted 9 March, 2020. This research was funded by the Ministry of Research, Technology and Higher Education of Indonesia in 2019. This research was performed in cooperation with the Institution.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.5755/j01.eie.26.3.23950[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]