Main Article Content
In this work we propose a function that allows to calculate a summary code from the parameters of a voice signal. This function is based on ordering of spectral coefficients obtained by means of the application of the Fast Fourier Transform (FFT), using a locally generated reference function (Gaussian random noise). The proposed method is oriented to the verification of integrity in forensic voice signals. The proposed methodology has a perceptual approach, which implies that the resulting code is maintained, even when modifications are made, particularly those that do not affect the sensitive content of the signal, such as re-quantization processes.
This work is licensed under a Creative Commons Attribution 4.0 International License.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
 H. Malik, “Acoustic environment identification and its applications to audioforensics,”IEEE Transactions on Information Forensics and Security, vol. 8,no. 11, pp. 1827–1837, 2013.
 M. Ayoob and W. Adi, “Improving system reliability by joint usage of hashfunction bits and error correction coding,” inEmerging Security Technologies(EST), 2015 Sixth International Conference on. IEEE, 2015, pp. 1–6.
 M. Zamani and A. B. A. Manaf, “Genetic algorithm for fragile audio wa-termarking,”Telecommunication Systems, vol. 59, no. 3, pp. 291–304, 2015.
 J. Fridrich and M. Goljan, “Robust hash functions for digital watermarking,”inInformation Technology: Coding and Computing, 2000. Proceedings. In-ternational Conference on. IEEE, 2000, pp. 178–183.
 B. Lei, Y. Soon, and E.-L. Tan, “Robust svd-based audio watermarking sche-me with differential evolution optimization,”IEEE transactions on audio,speech, and language processing, vol. 21, no. 11, pp. 2368–2378, 2013.
 I. M. Maung, Y. Tew, and K. Wong, “Authentication for aac compressed au-dio using data hiding,” inConsumer Electronics-Taiwan (ICCE-TW), 2016IEEE International Conference on. IEEE, 2016, pp. 1–2.
 J. Haitsma, T. Kalker, and J. Oostveen, “Robust audio hashing for contentidentification,” inInternational Workshop on Content-Based Multimedia In-dexing, vol. 4. Citeseer, 2001, pp. 117–124.
 J. Li, H. Wang, and Y. Jing, “Audio perceptual hashing based on nmf andmdct coefficients,”Chinese Journal of Electronics, vol. 24, no. 3, pp. 579–588,2015.
 M. S. Jain, M. V. Doshi, and M. T. Goyal, “Cryptanalytic jh and blakehash function for authentication and proposed work over blake-256 using c,”INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNO-LOGY, vol. 1, no. 4, pp. 1862–1866, 2013.
 R. Sobti and G. Geetha, “Cryptographic hash functions: a review,”IJCSIInternational Journal of Computer Science Issues, vol. 9, no. 2, pp. 461–479,2012.
 C. Qin, X. Chen, D. Ye, J. Wang, and X. Sun, “A novel image hashing sche-me with perceptual robustness using block truncation coding,”InformationSciences, vol. 361, pp. 84–99, 2016.
 X. Lv and Z. J. Wang, “Perceptual image hashing based on shape contextsand local feature points,”IEEE Transactions on Information Forensics andSecurity, vol. 7, no. 3, pp. 1081–1093, 2012.
 A. Neelima and K. M. Singh, “A short survey on perceptual hash function,”ADBU Journal of Engineering Technology, vol. 1, 2014.
 Z. Qiu-yu, R. Zhan-wei, X. Peng-fei, H. Yi-bo, and Y. Shuang, “Securityanalysis of speech perceptual hashing authentication algorithm,”Internatio-nal Journal of Security and Its Applications, vol. 10, no. 1, pp. 103–118, 2016.
 H. E. Michail, A. Kotsiolis, A. Kakarountas, G. Athanasiou, and C. Goutis,“Hardware implementation of the totally self-checking sha-256 hash core,” inEUROCON 2015-International Conference on Computer as a Tool (EURO-CON), IEEE. IEEE, 2015, pp. 1–5.
 N. Chidambaram, P. Raj, K. Thenmozhi, and R. Amirtharajan, “Enhancingthe security of customer data in cloud environments using a novel digital fin-gerprinting technique,”International Journal of Digital Multimedia Broad-casting, vol. 2016, p. 1, 2016.
 D. Tomović, I. Ognjanović, and R. Šendelj, “Security challenges of integrationof hash functions into cloud systems,” inEmbedded Computing (MECO),2015 4th Mediterranean Conference on. IEEE, 2015, pp. 110–114.
 D. M. Ballesteros L., D. Renza, and S. Camacho, “An unconditionally securespeech scrambling scheme based on an imitation process to a gaussian noisesignal,”J. Inf. Hiding Multimedia Sig. Process, vol. 7, no. 2, pp. 233–242,2016.
 D. M. Ballesteros L, D. Renza, and S. Camacho, “High scrambling degree inaudio through imitation of an unintelligible signal,”Lecture Notes in Com-puter Science, vol. 9703, pp. 251–259, 2016.
 D. M. Ballesteros L., D. Renza, and S. Camacho, “Security analysis of thespeech scrambling method based on imitation of a super-gaussian signal,”J.Inf. Hiding Multimedia Sig. Process, vol. 8, no. 1, pp. 156–167, 2017.