Abstract
Research on audio tampering detection and recovery plays an important role in the field of audio integrity, and authenticity certification. Generally, we use technology of fragile/semi fragile watermarking to detect and recover tampered audio. In this study, a new scheme for watermark embedding, tampering detection, and recovery is proposed. In the new scheme, we get the compressed version of original audio signal using compressed sensing technology and apply discrete wavelet transform (DWT) to each audio frame. In process of embedding, a new self-adaptive algorithm is proposed. Watermark is the quantized reference value of original framed audio signal and tampering location data, and is embedded in the region with low energy of high frequency coefficients and high energy of low frequency coefficients respectively after 2-level DWT. In process of detection, we locate tampered areas by comparing the value of generated random number and extracted watermark after XOR operation with the extracted location data. As for speech, we set a threshold to judge whether it is tampered or not. At last, we extract watermark in areas which are not damaged and get the recovered signal after decompression. Experiments and analysis show that signal after embedding has at least 5 dB higher average signal-to-noise ratio than others, and broken frames and groups can be detected exactly. When signal is destroyed by 20%, 98% of the corpus is intelligible after recovery, and even destroyed by 50%, 80% of the corpus recovered is also intelligible. Compared with other recovery algorithms, audio signal recovered by our proposal has a higher signal-to-noise ratio and a better robustness to some signal processing. When tampering rate is 50%, the average detection rate is over 93%, which indicates that our method is workable.
Similar content being viewed by others
References
Boney L, Tewfik AH, Handy KN (1996) Digital watermarks for audio signals. Proc 3rd IEEE Int Conf Multimedia Comput Syst 535015:473–480
Bravo-Solorio S, Calderon F, Li C-T (2018) Fast fragile watermark embedding and iterative mechanism with high self-restoration performance. Digital Signal Proc 73:83–92
Caldelli R, Filippini F, Becarelli R (2010) Reversible watermarking techniques: an overview and a classification, EURASIP J. Inf Secur 2010:1–19
Chen YF, Sun C, Shen JQ et al (2016) Semi-fragile watermarking for automatic detection of engineering drawing modifications in collaborative design. Comp-Aided Des Appl 56(10):1–16
Cox IJ, Kilian J, Leighton FT, Shamon T (1997) Secure spread spectrum: watering for multimedia, IEEE trans. Image Process 6(12):1673–1687
Dadkhah S, Manaf AA et al (2014) An effective SVD-based image tampering detection and self-recovery using active watermarking. Signal Process Image Commun 29(10):1197–1210
Diego R, Ballesteros L, Dora M, Camilo L (2018) Authenticity verification of audio signals based on fragile watermarking for audio forensics. Expert Syst Appl 91:211–222
Fridrich J (2002) Security of fragile authentication watermarks with localization. Int Soc Opt Photon 4675:691–700
He H, Chen F, Tai HM, Kalker T (2012) Performance analysis of a block neighborhood based self-recovery fragile watermarking scheme. IEEE Trans Inf Furans Secure 7(1):185–196
Horng S-J, Rosiyadi D, Li T, Takao T, Guo M, Khan MK (2013) A blind image copyright protection scheme for e-government. J Vis Commun Image Res 24:1099–1105
Hu HT, LEE TT (2019) Hybrid blind audio watermarking for proprietary protection, tamper proofing, and self-recovery. IEEE Access 7:180395–180408
Hua G, Huang JW, Shi YQ, Goh J (2016) Twenty years of digital audio watermarking comprehensive review. Signal Process 128:222–242
Kaur A, Dutta MK, Soni KM, Taneja N (2017) Localized & self-adaptive audio watermarking algorithm in the wavelet domain. J Inform Sec Appl 33:1–15
Khalil M, Adib A (2014) Audio watermarking with high embedding capacity based on multiple access techniques. Digital Signal Proc 116–125:116–125
Kim C, Shin D, Yang C-N (2018) Self-embedding fragile watermarking scheme to restoration of a tampered image using AMBTC. Pers Ubiquit Comput 22(1):11–22
Korus P, Ditech A (2013) Efficient method for content reconstruction with self-embedding, IEEE trans. Image Process 22(3):1134–1147
Kumar S, Singh BK, Yadav M (2020) A recent survey on multimedia and database watermarking. Multimedia Tools Appl 79:1–49
Larbi SD, Jaidane-Saidane M (2005) Audio watermarking: a way to stationarize audio signals. IEEE Trans Signal Process 53(2):816–823
Lei BY, Soon IY, Li Z (2001) Blind and robust audio watermaring scheme based on SVD-DCT. Signal Process 91:1973–1984
Lin P-L, Huang P-W, Peng AW (2004) A fragile watermarking scheme for image authentication with localization and recovery. IEEE Six International Symposium on Multimedia Software Engineering 9:146–153
Liu ZH, Luo D, Huang JW (2017) Tamper recovery algorithm for digital speech signal based on DWT and DCT. Multimed Tools Appl 76(10):12481–12504
Nair U, Gajanan K, Birajdar (2020) Compressed domain secure, robust and high-capacity audio watermarking. Iran J Comp Sci 1-16:217–232
Nishimura R (2012) Audio watermarking using spatial masking and ambisonics. IEEE Trans Audio Speech Lang Process 20(9):2461–2469
Ortiz AM, Uribe CF, Hernandez JJG (2019) Framework for audio reversible watermarking robust against content replacement with signal restoration capabilities. J Frankl Inst 356:6793–6816
Patra B, Patra JC (2012) Crt-based fragile self-recovery watermarking scheme for image authentication and recovery. IEEE international symposium on intelligent signal processing and communication systems 6473528:430–435
Petitcolas FAP, Anderson RJ, Kuhn MG (1999) Information hiding-a survey. Proc.IEEE 87(7):1062–1078
Qian Q, Wang H (2018) Speech authentication and content recovery scheme for security communication and storage. Telecommun Syst 67(4):635–649
Renza D, Dora M, Ballesteros L, Lemus C (2018) Authenticity verification of audio signals based on fragile watermarking for audio forensics. Expert Syst Appl 91:211–222
Renza D, Dora M, Camilo BL, Lemus (2018) Authenticity verification of audio signals based on fragile watermarking for audio forensics. Expert Syst Appl 91:211–222
Rosiyadi D, Horng S-J, Fan P, Wang X, Khan MK, Yi P (2012) Copyright protection for e-government document images. IEEE MultiMedia 19(3):62–73
Sarreshtedari S, Akhaee MA, Abbasfar A (2015) A watermarking method for digital speech self-Recovery. IEEE/ACM Trans Audio, Speech Language Proc 23(11):1917–1925
Shen JQ (2017) Research on image tampering detection and recovery based on fragile watermarking. Doctoral Dissertation of Huazhong University of science and technology, 25–36
Tang X (2015) Research on some key algorithms of digital audio watermarkiing. Doctoral Dissertation of Beijing University of Posts and Telecommunications, 32–35
Wang L, Lu K, Liu P (2014) Compressed sensing of a remote sensing image based on the priors of the reference image. IEEE Geosci Remote Sens Lett 12(4):736–740
Wang J, Liu Z, He J, Qi C (2016) An authentication and recovery scheme for digital speech signal based on DWT. In: International Workshop on Digital Watermarking. Springer, Cham, pp 206–219
Wang N, Li Z, Cheng X, Chen Y (2017) Dual watermarking algorithm based on singular value decomposition and compressive sensing. IEEE International Conference on Communication Technology 8359932:1763–1767
Wang S, Yuan W, Wang J, Unoki M (2019) Detection of speech tampering using sparse representations and spectral manipulations-based information hiding. Speech Comm 112:1–14
Yang Q (2012) Research on image tampering detection and copyright protection based on digital watermark. Doctoral Dissertation of Nankai University, 55–68
Zhang H, Cheng Y (2004) Detection and restoration algorithm of image self-embedding and tampering. Chin J Electron 32(2):196–199
Acknowledgements
This research is supported by the National Natural Science Foundation of China (No.NSFC61876131), and the Key Basic Research and Development of Ministry of Science and Technology (No.2018YFC0806802).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hu, Y., Lu, W., Ma, M. et al. A semi fragile watermarking algorithm based on compressed sensing applied for audio tampering detection and recovery. Multimed Tools Appl 81, 17729–17746 (2022). https://doi.org/10.1007/s11042-022-12719-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12719-0