Digital watermarking and tamper detection in speech signal using blind detection

Sharvari .; Vaishnavi D V; Dhanalakshmi R; Shikha Tripatha

doi:10.1117/12.2631575

1 May 2022 Digital watermarking and tamper detection in speech signal using blind detection

Sharvari ., Vaishnavi D V, Dhanalakshmi R, Shikha Tripatha

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 1217118 (2022) https://doi.org/10.1117/12.2631575
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

Advancement of digital signal processing and networking has raised many security and copyright concerns, thus it is very important to protect the authentication of digital data. In this work, an audio watermarking algorithm has been proposed which can be efficiently used for tamper detection and is also robust against reasonable attacks. Also, the watermarks are inaudible. The proposed algorithm can easily detect tampering as the watermarks are embedded at each frame without causing any audio degradation. In the proposed technique, first the audio signal is compressed using Graph Based Transform (GBT), for which watermarks are embedded into Line Spectral coefficients (LSFs) that are derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Watermarks thus embedded in all frames are not only inaudible to the Human auditory system but also potentially provide robustness against meaningful attacks. This work also focuses on Blind tamper detection which is made effortless due to the proposed embedding algorithm. To measure the robustness of the algorithm, general processing of watermarked signals was done along with fragility testing. Quality of the audio was measured using Perceptual Evaluation of Speech Quality (PESQ) and Short-time objective intelligibility (STOI). The maximum PESQ score and STOI score of 2.8781 and 0.8150 respectively was observed without any attack on the audio signal. Tamper detection and quality measurement are the major contributions of this work. Detailed metric evaluation for attacks such as Scaling, Resampling, Filtering, Compression and Addition of White Gaussian noise (AWGN) has been computed and compared. The proposed technique makes tamper identification easier and gives framewise security.

Citation Download Citation

Sharvari ., Vaishnavi D V, Dhanalakshmi R, and Shikha Tripatha "Digital watermarking and tamper detection in speech signal using blind detection", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 1217118 (1 May 2022); https://doi.org/10.1117/12.2631575

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KEYWORDS

Digital watermarking

Signal detection

Modulation

Quantization

Signal processing

Detection and tracking algorithms

Statistical analysis

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