Criptosistema esteganográfico de audio, basado en atractores caóticos y compresión de textos por medio de redes neuronales

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dc.contributor.advisorAlvarado Nieto, Luz Deicy
dc.contributor.advisorAmaya Barrera, Edilma Isabel
dc.contributor.authorRodríguez Galindo, Juan Felipe
dc.contributor.authorBohórquez Rodríguez, Mateo
dc.contributor.orcidAlvarado Nieto, Luz Deicy [0000-0002-1305-3123]
dc.contributor.orcidAmaya Barrera, Edilma Isabel [0000-0002-8845-5901]
dc.date.accessioned2025-08-05T22:45:56Z
dc.date.available2025-08-05T22:45:56Z
dc.date.created2025-07-11
dc.descriptionEste trabajo presenta un criptosistema esteganográfico de audio que combina la compresión inteligente de textos mediante redes neuronales y la encriptación con atractores caóticos, integrando estas técnicas en un proceso de inserción imperceptible dentro de archivos WAV. La arquitectura propuesta emplea el modelo LLMLingua para reducir la longitud de los mensajes sin comprometer su semántica, y mapas logísticos caóticos para generar claves pseudoaleatorias que incrementan la seguridad del cifrado. El mensaje comprimido y cifrado es ocultado mediante modificación de bits menos significativos, utilizando técnicas de inserción secuencial o aleatoria. El sistema fue validado a través de métricas objetivas como PSNR, MSE, entropía y pruebas estadísticas, demostrando alta imperceptibilidad y robustez frente a ataques de compresión, filtrado, escalado y eco. Sin embargo, se identificaron vulnerabilidades frente a ruido gaussiano y remuestreo agresivo. Esta investigación contribuye al desarrollo de tecnologías de seguridad digital, proponiendo un modelo funcional y adaptable a contextos reales donde la confidencialidad es esencial.
dc.description.abstractThis research proposes an audio steganographic cryptosystem that integrates intelligent text compression through neural networks with encryption based on chaotic attractors. The proposed system leverages LLMLingua to reduce input message size while preserving semantic integrity and applies logistic chaotic maps to generate pseudo-random keys for XOR-based encryption. The compressed and encrypted message is embedded into WAV audio files using least significant bit (LSB) manipulation, via sequential or random insertion guided by chaotic sequences. Performance was evaluated through objective metrics such as PSNR, MSE, entropy, and statistical tests, showing high imperceptibility and resilience against compression, filtering, amplitude scaling, and echo attacks. However, it exhibits weaknesses under Gaussian noise and aggressive resampling. This work contributes to digital security by offering a robust and adaptable model for secure information embedding in audio signals within real-world communication scenarios.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/98379
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas.
dc.relation.referencesRohit Tanwar, K. S. (2019). An Optimized Approach for Secure Data Transmission Using Spread Spectrum Audio Steganography, Chaos Theory, and Social Impact Theory Optimizer. Journal of Computer Networks and Communications, DOI: 10.1155/2019/5124364 .
dc.relation.referencesEco, U. (2010). El nombre de la rosa. Milan: Penguin Random House Grupo Editorial España.
dc.relation.referencesUrgellés, J. G. (2010). Matemáticos, Espías y Piratas Informáticos. Barcelona, España: RBA Libros.
dc.relation.referencesHalicarnaso, H. d. (1978). Los nueve libros de la historia. Madrid: Universidad Complutense de Madrid.
dc.relation.referencesCárdenas, M. A. (2020). CRIPTOSISTEMA ESTEGANOGRÁFICO A TRAVÉS DE IMÁGENES DIGITALES PARA EL CIFRADO DE TEXTO USANDO TEORÍA DEL CAOS Y AUTÓMATAS CELULARES. Bogotá D.C., Colombia: Universidad Distrital Francisco José de Caldas.
dc.relation.referencesJepri Banjarnahor, e. a. (2019). Audio Steganography Applications Using Auditory Features Watermarking. Journal of Physics: Conference Series, DOI: 10.1088/1742- 6596/1230/1/012073 .
dc.relation.referencesPugazhenthi, R. D. (2016). Ideal Sampling Rate to Reduce Distortion in Audio Steganography . Procedia Computer Science, Volumen 85, DOI: 10.1016/j.procs.2016.05.185.
dc.relation.referencesSaravanan, S. D. (2019). VoIP Steganography Methods, a Survey . Bulgarian Academy of Sciences Cybernetics and Information, DOI: 10.2478/cait-2019-0004.
dc.relation.referencesAhmed A. AlSabhany, e. a. (2020). Digital Audio Steganography: Systematic Review, Classification, and Analysis of the Current State of the Art . Computer Science Review, DOI: 10.1016/j.cosrev.2020.100316 .
dc.relation.referencesMohammed Ishaq Hussain, e. a. (2020). Audio Steganography Based on LSB Encryption and Chaos Encryption . Journal of Engineering Science, ISSN: 0377-9254 .
dc.relation.referencesShunzhi Jiang, e. a. (2020). SmartSteganography: Light-weight Generative Audio Steganography Model for Smart Embedding Application . Science Direct, DOI: https://doi.org/10.1016/j.jnca.2020.102689 .
dc.relation.referencesMohammed J. Alhaddad, e. a. (2020). Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things . Symmetry, DOI: 10.3390/sym12122071 .
dc.relation.referencesELSHOUSH, M. M. (2022). Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography—An Innovative Approach . IEEE Explore, DOI:10.1109/ACCESS.2022.3155146.
dc.relation.referencesMuhammad Fahad Khan, e. a. (2016). Steganography between Silence Intervals of Audio in Video Content Using Chaotic Maps. Circuits, Systems, and Signal Processing, DOI: 10.48550/arXiv.1610.04346 .
dc.relation.referencesClelland, C. T., Risca, V., & Bancroft, C. (1999). Hiding messages in DNA microdots. Nature, 533-534.
dc.relation.referencesProvos, N., & Honeyman, P. (2023). Hide and Seek: An Introduction to Steganography. IEEE Security & Privacy, 32-44.
dc.relation.referencesXinpeng, Z., & Wang, S. (2006). Vulnerability of Pixel-Value Differencing Steganography to Histogram Analysis and Modification for Enhanced Security. Pattern Recognition Letters, 681-687.
dc.relation.referencesWu, H. C., & Tsai, C. C. (2009). Chaos-based image encryption scheme with variable control parameters. Pattern Recognition Letters, 1105-1111.
dc.relation.referencesDhanaraj, R. S. (2017). Enhanced blend of image steganography and cryptography. International Conference on Computing Methodologies and Communication (ICCMC), (págs. pp. 230-235,doi: 10.1109/ICCMC.2017.8282682.). Erode, India.
dc.relation.referencesDorigo , M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.
dc.relation.referencesEberhart, R., & Kennedy, J. (1995). Particle Swarm Optimization. Proceedings of ICNN'95- international conference on neural networks, 1942-1948.
dc.relation.referencesMay, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 459-467.
dc.relation.referencesLorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal of the atmospheric sciences, 130-141.
dc.relation.referencesHaykin, S. (2009). Neural networks and learning machines. Pearson Education.
dc.relation.referencesZahoor, A. S., Mahmood, H. S., & Javed, A. (2016). Information security management needs more holistic approach: A literature review. International Journal of Information Management, 215-225.
dc.relation.referencesHeidari-Bateni, G., & McGillem, C. (1994). A chaotic direct-sequence spread-spectrum communication system. IEEE Transactions on Communications, 1524-1527.
dc.relation.referencesM. Meyer, C., & P.V.S., A. (2019). Data-efficient Neural Text Compression with Interactive Learning. Association for Computational Linguistics, 2543–2554.
dc.relation.referencesIan Goodfellow, Y. B. (2016). Deep Learning. MIT Press.
dc.relation.referencesSepp Hochreiter, J. S. (1997). Long Short-Term Memory. Neural Comput , 9 (8): 1735–1780 doi: https://doi.org/10.1162/neco.1997.9.8.1735.
dc.relation.referencesAshish Vaswani, I. P. (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA.
dc.relation.referencesDiederik P. Kingma, M. W. (2013). Auto-Encoding Variational Bayes. Obtenido de arXiv preprint arXiv:1312.6114.
dc.relation.referencesRadford, A. W. (2019). Language Models are Unsupervised Multitask Learners.
dc.relation.referencesKatz, B., & Katz, R. A. (2002). Mastering Audio: The Art and the Science. Oxford: Focal press.
dc.relation.referencesWatkinson, J. (1994). The art of digital audio. Oxford: Focal Press.
dc.relation.referencesVaseghi, S. V. (2007). Multimedia Signal Processing. Hoboken, United States: Wiley.
dc.relation.referencesSchwartz, E., Romney, J., & Burg, J. (2016). Digital Audio Concepts, Applications and Science. Portland, Oregon, United States: Franklin, Beedle & Associates Inc.
dc.relation.referencesKatzenbeisser, S., & P. Petitcolas, F. A. (2000). Information Hiding Techniques for Steganography and Digital Watermarking. Artech House Books.
dc.relation.referencesBender, W., Gruhl, D., Morimoto, N., & Lu, A. (1996). Techniques for data hiding. IBM Systems Journal.
dc.relation.referencesGruhl, D., Lu, A., & Bender, W. (1996). Echo hiding. Springer Nature: Lecture Notes in Computer Science.
dc.relation.referencesChen, X.-S., Zhang, X., Gao, C., & Yang, Y.-T. (2009). Audio watermarking system based on detection theory of chaotic phase transformation. Guilin, China: IEEE.
dc.relation.referencesMarvel, L. M., Boncelet, C. G., & Retter, C. T. (1999). Spread spectrum image steganography. IEEE Transactions on Image Processing.
dc.relation.referencesFatiha, D., Beghdad, A., Karim, A.-m., & Habib, H. (2012). Comparative Study of Digital Audio Steganography Techniques. EURASIP Journal on Audio, Speech, and Music Processing.
dc.relation.referencesPoulami, D., Swarnendu, M., Debashis, G., Debnath, B., & Samir, B. (2008). researchgate. Obtenido de researchgate: https://www.researchgate.net/publication/228691665_A_tutorial_review_on_stegan ography
dc.relation.referencesSingh, P. K., & Aggrawal, R. K. (2010). Enhancement of LSB based Steganography for Hiding Image in Audio. International Journal on Computer Science and Engineering.
dc.relation.referencesRamana Naik, B. V., & S. Sumapriya, P. S. (20 de 08 de 2024). INTERNATIONAL JOURNAL & MAGAZINE OF ENGINEERING, TECHNOLOGY, MANAGEMENT AND RESEARCH. Obtenido de Decimated Wavelet with Spread Spectrum Approach for Audio Steganography: http://www.yuvaengineers.com/decimated-wavelet-with- spread-spectrum-approach-for-audio-steganography-b-venkata-ramana-naik-p-s-s- sumapriya/
dc.relation.referencesAngel Angel , J. d. (20 de 08 de 2014). MathCon, The Mathematics Firm. Obtenido de http://www.math.com.mx/criptografia.html
dc.relation.referencesMoreno, J., Fabio, P., Huérfano, R., Suarez, C., & Amaya, I. (2015). Modelo de encriptación simétrica basada en atractores caóticos. Ingeniería, 378-390.
dc.relation.referencesSadderdaze. (20 de 08 de 2024). Seguridad y Alta Disponibilidad. Obtenido de https://error595.wordpress.com/2020/03/13/stego/
dc.relation.referencesDaniel Isac Escobedo Beceiro, G. J. (2022). How to classify newborn cry through a supervised neural network. Revista Cubana de Informática Médica.
dc.relation.referencesRivera, M. (2018). Introducción a Redes Neuronales Recurrentes (RNN). Obtenido de Centro de investigación en matempáticas de México, portal personal: http://personal.cimat.mx:8181/~mrivera/cursos/aprendizaje_profundo/RNN_LTSM/ introduccion_rnn.html
dc.relation.referencesGleick, J. (1987). CHAOS. Virginia: Viking Penguin Inc.
dc.relation.referencesLorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal of the Atmospheric Sciences, 130-141.
dc.relation.referencesSantiago Netto, R. (5 de 2000). fisicanet. Obtenido de fisicanet.com.ar: https://www.fisicanet.com.ar/biografias/nobelfisica/biografias-fisica-2/gell-mann- murray.php
dc.relation.referencesMicrosoft Research. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. Singapore: Association for Computational Linguistics.
dc.relation.referencesJiang, H., Wu, Q., Lin, C.-Y., Yang, Y., & Qiu, L. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. Singapore: Association for Computational Linguistics.
dc.relation.referencesMicrosoft. (18 de 2 de 2024). LLMLingua. Obtenido de github.com: https://github.com/microsoft/LLMLingua
dc.relation.referencesMohammed, I. A. (2020). ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY: A SYSTEMATIC MAPPING OF LITERATURE. International Journal of Innovations in Engineering Research and Technology (IJIERT), Vol. 7, No. 09, 2394-369.
dc.relation.referencesMohammed, I. A. (2020). Artificial Inteligence For Cybersecurity:A. International Journal of Innovations in Engineering Research and Technology (IJIERT), Vol. 7, No. 09, 2394-3696.
dc.relation.referencesMahmoud M., M., & Huwaida T, E. (2022). Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach. IEEE Access, 29954-29971.
dc.relation.referencesAbood, E., Abdullah, A., Abduljabbar, Z., Ahmad, S., Al Sibahee, M., Ghrabta, J., . . . Wahab, E. (2022). Audio steganography with enhanced LSB method for securing 96 encrypted text with bit cycling. Bulletin of Electrical Engineering and Informatics, 185-194.
dc.relation.referencesAl Sibahee, M., Abduljabbar, Z., Luo, C., Zhang, J., Huang, Y., Abduljaleel, I., . . . Nyangaresi, V. O. (2024). Hiding scrambled text messages in speech signals using a lightweight hyperchaotic map and conditional LSB mechanism. PLoS One, e0296469.
dc.relation.referencesOuazzane, K., Phipps, A., & Vassilev, V. (2022). Securing Voice Communications Using Audio Steganography. International Journal of Computer Network and Information Security.
dc.relation.referencesNasr, M., El-Shafai, W., El-Rabaie, E.-S., El-Fishawy, A., El-Hoseny, H., Abd El-Samie, F., & Abdel-Salam, N. (2024). A robust audio steganography technique based on image encryption using different chaotic maps. Scientific Reports, 2045-2322.
dc.relation.referencesJing, L., Kaixi, W., & Xiaozhu, J. (2023). A Coverless Audio Steganography Based on Generative Adversarial Networks. Electronics.
dc.relation.referencesJiménez-Rodríguez, M., & Flores-Siordia, O. (2015). Sistema para codificar información implementando varias órbitas caóticas. Ingeniería Investigación y Tecnología, 335- 343.
dc.relation.referencesJohnson, M., & Lakoff, G. (2008). Metaphors we lived by. Chicago: University of Chicago Press.
dc.relation.referencesSingh, S. (1999). The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography. Londres: Fourth Estate.
dc.relation.referencesEnas Wahab Abood, e. a. (2022). Audio Steganography with Enhanced LSB Method for Securing Encrypted Text with Bit Cycling. Bulletin of Electrical Engineering and Informatics, DOI: 10.11591/eei.v11i1.3279.
dc.relation.referencesAbdullah, M., Kaan, I., Resit, S., & Yu, B. (2024). NoiseAttack: An Evasive Sample-Specific Multi-Targeted Backdoor Attack Through White Gaussian Noise.
dc.relation.referencesWen-Nung, L., & Chang, L.-C. (2006). Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification. Multimedia, IEEE Transactions on, 46-59.
dc.relation.referencesHossen, I., & Hei, X. (2022). aaeCAPTCHA: The Design and Implementation of Audio Adversarial CAPTCHA. 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P) (págs. 430-447). IEEE.
dc.relation.referencesZar Tun, P., & Thand Swe, K. (2020). AUDIO SIGNAL FILTERING WITH LOW-PASS AND HIGH-PASS FILTERS. IJCIRAS, 1-4.
dc.relation.referencesSandler, M., Benetos, E., & Subramanian, V. (2019). ROBUSTNESS OF ADVERSARIAL ATTACKS IN SOUND EVENT CLASSIFICATION. Detection and Classification of Acoustic Scenes and Events 2019.
dc.relation.referencesXiang , L., Pin-Yu, C., & Wenqi, W. (2025). Measuring the Robustness of Audio Deepfake Detectors.
dc.relation.referencesYizhu, W., Ashwin, I., Bien Ramos, A., Guo, H., & Yan, Q. (2025). SoK: How Robust is Audio Watermarking in Generative AI models?
dc.relation.referencesChen, G., Wu, Y., Liu, S., Liu, T., Du, X., & Wei, F. (2024). WavMark: Watermarking for Audio Generation.
dc.relation.referencesWen-Nung, L., & Li-Chun, C. (2006). Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification. IEEE Transactions on Multimedia.
dc.relation.referencesHaolin, W., Chen, J., Du, R., Cong, W., Kun, H., Shang, X., . . . Xu, G. (2024). CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning.
dc.relation.referencesShu , W., Sun, K., & Qi, L. (2024). Compensating Removed Frequency Components: Thwarting Voice Spectrum Reduction Attacks.
dc.relation.referencesBaig, A., Moinuddin, A., & Khan, E. (11 de 2019). PSNR of Highest Distortion Region: An Effective Image Quality Assessment Method. 1-4. doi:10.1109/UPCON47278.2019.8980171
dc.relation.referencesGarcía Marquéz, G. (1981). Crónicas de una muerte anunciada. Bogotá D.C., Colombia: La Oveja Negra.
dc.relation.referencesBrandenburg, K. &. (1994). ISO-MPEG-1 Audio: A generic standard for coding of high- quality digital audio. Journal of the Audio Engineering Society, 42(10), 780–792.
dc.relation.referencesMontgomery, C. (2004). Vorbis I specification. Xiph.Org Foundation.
dc.relation.referencesKhaled, A. E.-R.-S. (2024). A robust audio steganography technique based on chaotic maps and inverse STFT. Scientific Reports, 14(1), 12567.
dc.relation.referencesSayed, H. M. (2023). Information security for audio steganography using a phase coding method. Egyptian Journal of Telecommunications and Applied Sciences, 41(2), 112– 121.
dc.relation.referencesDevelopers, T. s.-l. (6 de 10 de 2025). sklearn.metrics.mean_squared_error. Obtenido de Scikit-learn: https://www.google.com/search?q=https://docs.scikit- learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
dc.relation.referencesDevelopers, T. s.-l. (2025). sklearn.metrics.mean_squared_error. Recuperado el 10 de 6 de 2025, de Scikit-learn: https://www.google.com/search?q=https://docs.scikit- learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
dc.relation.referencesDevelopers, T. s.-i. (2025). skimage.metrics.peak_signal_noise_ratio. Recuperado el 10 de 6 de 2025, de scikit-image: https://www.google.com/search?q=https://scikit- image.org/docs/stable/api/skimage.metrics.html%23skimage.metrics.peak_signal_n oise_ratio
dc.relation.referencesDevelopers, T. s.-l. (2025). sklearn.metrics.mean_absolute_error. Recuperado el 10 de 6 de 2025, de Scikit-learn: https://www.google.com/search?q=https://docs.scikit- learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html
dc.relation.referencesDevelopers, T. S. (2025). scipy.stats.entropy. Recuperado el 10 de 6 de 2025, de SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
dc.relation.referencesDevelopers, T. S. (2025). scipy.stats.chisquare. Recuperado el 10 de 6 de 2025, de SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html
dc.relation.referencesDevelopers, T. S. (2025). scipy.stats.ks_2samp. Recuperado el 10 de 6 de 2025, de SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html
dc.relation.referencesDevelopers, T. S. (2025). scipy.stats.mannwhitneyu. Recuperado el 10 de 6 de 2025, de SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html
dc.relation.referencesCommission, I. E. (2008). IEC 80000-13: Quantities and units – Part 13: Information science and technology. Recuperado el 12 de 6 de 2025, de https://webstore.iec.ch/publication/2225
dc.relation.referencesMann, H. B., & Whitney, D. R. (1946). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 50-60.
dc.relation.referencesMolina Arias, M. (2017). ¿Qué significa realmente el valor de p? Revista Pediatría de Atención Primaria, 377-381.
dc.relation.referencesDaniel J., B., James O., B., Magnus, J., Brian A., N., E.-J., W., Richard, B., . . . Thomas D., C. (2017). Redefine statistical significance. Nature Human Behaviour.
dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectEsteganografía de audio
dc.subjectCriptografía caótica
dc.subjectRedes neuronales
dc.subjectCompresión de texto
dc.subjectAtractores caóticos
dc.subjectInserción LSB
dc.subject.keywordAudio steganography
dc.subject.keywordChaotic cryptography
dc.subject.keywordNeural networks
dc.subject.keywordText compression
dc.subject.keywordChaotic attractors
dc.subject.keywordLSB embedding
dc.subject.lembIngenería de Sistemas -- Tesis y disertaciones acadéemicas
dc.subject.lembEsteganografía en audio
dc.subject.lembRedes neuronales
dc.subject.lembSeguridad digital
dc.titleCriptosistema esteganográfico de audio, basado en atractores caóticos y compresión de textos por medio de redes neuronales
dc.title.titleenglishAudio steganographic cryptosystem, based on chaotic attractors and text compression using neural networks
dc.typebachelorThesis
dc.type.degreeInvestigación-Innovación
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

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