La stéganographie numérique est une technique récente qui a émergé comme une source importante pour la sécurité des données. Elle consiste à envoyer secrètement et de manière fiable des informations dissimulées et non pas seulement à masquer leur présence. Elle exploite les caractéristiques des fichiers médias numériques anodins, tels que l’image, le son et la vidéo,et les utilise comme support pour véhiculer des informations secrète d’une façon inaperçue. Les techniques de cryptage et de tatouage sont déjà utilisées pour résoudre les problèmes liés à la sécurité des données. Toutefois,l’évolution des tentatives d’interception et de déchiffrement des données numériques nécessitent de nouvelles techniques pour enrayer les tentatives malveillantes et d’élargir le champ des applications y associées. L’objectif principal des systèmes stéganographiques consiste à fournir de nouveaux moyens sécurisés, indétectables et imperceptibles pour dissimuler des données.La stéganographie est utilisée sous l’hypothèse qu’elle ne sera pas détectée si personne n’essaye de la découvrir. Les techniques récentes destéganographie ont déjà été employées dans diverses applications. La majorité de ces applications ont pour objectif d’assurer la confidentialité des données.D’autres par contre sont utilisées malicieusement. L’utilisation de la stéganographie à des fins criminelles, de terrorisme, d’espionnage ou de piraterie constitue une menace réelle. Ces tentatives malveillantes de communiquer secrètement ont récemment conduit les chercheurs à inclure une nouvelle branche de recherche: la stéganalyse, pour contrer les techniques stéganographique. L’objectif principal de la stéganalyse est de détecter la résence potentielle d’un message dissimulé dans un support numérique et ne considère pas nécessairement son extraction. La parole numérique constitue un terrain de prédilection pour dissimuler des données numériques. En effet, elle est présente en abondance grâce aux technologies de télécommunications fixes ou mobiles et aussi à travers divers moyens de stockage de l’audio numérique. Cette thèse étudie la stéganographie et la stéganalyse utilisant la voix numérisée comme support et vise à (1) présenter un algorithme qui répond aux exigences des systèmes stéganographiques reliées à la capacité élevée, l’indétectabilité et l’imperceptibilité des données dissimulées, (2) le contrôle de la distorsion induite par le processus de dissimulation des données (3) définir un nouveau concept de zones spectrales dans le domaine de Fourier utilisant et l’amplitude et la phase (4) introduire un nouveau algorithme de stéganalyse basé sur les techniques de compression sans pertes d’information à la fois simple et efficace. La performance de l’algorithme stéganographique est mesurée par des méthodes d’évaluation perceptuelles et statistiques. D’autre part, la performance de l’algorithme de stéganalyse est mesurée par la capacité du système à distinguer entre un signal audio pur et un signal audio stéganographié. Les résultats sont très prometteurs et montrent des compromis de performance intéressants par rapport aux méthodes connexes. Les travaux futurs incluent essentiellement le renforcement de l’algorithme de stéganalyse pour qu’il soit en mesure de détecter une faible quantité de données dissimulées. Nous planifions également l’intégration de notre algorithme de stéganographie dans certaines plateformes émergentes telles que l’iPhone. D’autres perspectives consistent à améliorer l’algorithme stéganographique pour que les données dissimulées soit résistantes au codage de la parole, au bruit et à la distorsion induits parles canaux de transmission. / Digital steganography is a young flourishing science emerged as a prominent source of data security. The primary goal of steganography is to reliably send hidden information secretly, not merely to obscure its presence. It exploits the characteristics of digital media files such as: image, audio, video, text by utilizing them as carriers to secretly communicate data. Encryption and watermarking techniques are already used to address concerns related to datasecurity. However, constantly-changing attacks on the integrity of digital data require new techniques to break the cycle of malicious attempts and expand the scope of involved applications. The main objective of steganographic systems is to provide secure, undetectable and imperceptible ways to conceal high-rate of data into digital medium. Steganography is used under the assumption that it will not be detected if no one is attempting to uncover it. Steganography techniques have found their way into various and versatile applications. Some of these applications are used for the benefit of people others are used maliciously. The threat posed by criminals, hackers, terrorists and spies using steganography is indeed real. To defeat malicious attempts when communicating secretly, researchers’ work has been lately extended toinclude a new and parallel research branch to countermeasure steganagraphy techniques called steganalysis. The main purpose of steganalysis technique is to detect the presence or not of hidden message and does not consider necessarily its successful extraction. Digital speech, in particular, constitutes a prominent source of data-hiding across novel telecommunication technologies such as covered voice-over-IP, audio conferencing, etc. This thesis investigatesdigital speech steganography and steganalysis and aims at: (1) presenting an algorithm that meets high data capacity, undetectability and imperceptibility requirements of steganographic systems, (2) controlling the distortion induced by the embedding process (3) presenting new concepts of spectral embedding areas in the Fourier domain which is applicable to magnitude and phase spectrums and (4) introducing a simple yet effective speech steganalysis algorithm based on lossless data compression techniques. The steganographic algorithm’s performance is measured by perceptual and statistical evaluation methods. On the other hand, the steganalysis algorithm’s performance is measured by how well the system can distinguish between stego- and cover-audio signals. The results are very promising and show interesting performance tradeoffs compared to related methods. Future work is based mainly on strengthening the proposed steganalysis algorithm to be able to detect small hiding capacity. As for our steganographic algorithm, we aim at integrating our steganographic in some emerging devices such as iPhone and further enhancing the capabilities of our steganographic algorithm to ensure hidden-data integrity under severe compression, noise and channel distortion.

Background: Audio signal reconstruction using machine/deep learning algorithms has been explored much more in the recent years, and it has many applications in digital signal processing. There are many research works on audio reconstruction with linear interpolation, phase coding, tone insertion techniques combined with AI models. However, there is no research work on reconstructing audio signals with the fusion of Steganoflage (an adaptive approach to image steganography)  and AI models. Thus, in our thesis work, we focus on audio reconstruction combining Steganoflage and AI models. Objectives: This thesis aims to explore the possible enhancement of audio reconstruction using machine/deep learning models fusing Steganoflage technique. Furthermore, the suitable models implemented with the fusion of Steganoflage are analyzed and compared based on the performance metrics. Methods: We have conducted a systematic literature review followed by an experiment method to answer our research questions. The models implemented in the thesis are the results from a systematic literature review (SLR). In the experiments, we have fused the RF (Random Forest), SVR (Support Vector Regression), and LSTM (Long Short-Term Memory) models with Steganoflage for possible enhancement of reconstruction of lost audio signals. Then, the models were trained to estimate the possible approximate reconstructed signals. Finally, we observed the performance of the models and compared the reconstructed audio signals with the original signals (ground-truth) with four different performance metrics: Pearson linear correlation, PSNR, WPSNR, and SSIM. Results: The results from the SLR show that for machine learning models, RF and SVR models were mainly used for signals reconstructions and works well with time-series data. For deep learning models, recurrent neural network LSTM was the first choice as the survey of literature demonstrated that the model is suitable for time series forecasting. From the experiments, we found that the performance of LSTM model was better than RF and SVR models. Moreover, the reconstruction of audio signals from dropped short single region was better than that for multiple regions. Conclusions: We conclude that the Steganoflage, when fused with machine/deep learning models, enhances the lost audio signal reconstruction. Moreover, we also conclude that the LSTM model is more accurate than RF and SVR models in reconstructing the lost audio signals for a single drop region on both short and long gaps. However, we also observed that the audio reconstruction for multiple drops needs improvements considering long gaps. Furthermore, improvements can be made by exploring newer AI methods/optimization to enhance the reconstructed audio signals.

Background. Coverless steganography is a relatively unexplored area of steganography where the message is not embedded into a cover media. Instead the message is derived from one or several properties already existing in the carrier media. This renders steganalysis methods used for traditional steganography useless. Early coverless methods were applied to images or texts but more recently the possibilities in the video and audio domain have been explored. The audio domain still remains relatively unexplored however, with the earliest work being presented in 2022. In this thesis, we narrow the existing research gap by proposing an audio-compatible method which uses the timestamp that marks when a carrier media was received to generate a time-based key which can be applied to the hash produced by said carrier. This effectively allows one carrier to represent a range of different hashes depending on the timestamp specifying when it was received, increasing capacity. Objectives. The objectives of the thesis are to explore what features of audio are suitable for steganographic use, to establish a method for finding audio clips which can represent a specific message to be sent and to improve on the current state-of-the-art method, taking capacity, robustness and cost into consideration. Methods. A literature review was first conducted to gain insight on techniques used in previous works. This served both to illuminate features of audio that could be used to good effect in a coverless approach, and to identify coverless approaches which could work but had not been tested yet. Experiments were then performed on two datasets to show the effective capacity increase of the proposed method when used in tandem with the existing state-of-the-art method for coverless audio steganography. Additional robustness tests for said state-of-the-art method were also performed. Results. The results show that the proposed method could increase the per-message capacity from eight bits to 16 bits, while still retaining 100% effective capacity using only 200 key permutations, given a database consisting of 50 one-minute long audio clips. They further show that the time cost added by the proposed method is in total less than 0.1 seconds for 2048 key permutations. The robustness experiments show that the hashing algorithms used in the state-of-the-art method have high robustness against additive white gaussian noise, low-pass filters, and resampling attacks but are weaker against compression and band-pass filters.  Conclusions. We address the scientific gap and complete our objectives by proposing a method which can increase capacity of existing coverless steganography methods. We demonstrate the capacity increase our method brings by using it in tandem with the state-of-the-art method for the coverless audio domain. We argue that our method is not limited to the audio domain, or to the coverless method with which we performed our experiments. Finally, we discuss several directions for future works. / Bakgrund. Täcklös steganografi är ett relativt outforskat område inom steganografi där meddelandet, istället för att gömmas i ett medium, representeras av en eller flera egenskaper som kan erhållas från mediet. Detta faktum hindrar nuvarande steganalysmetoder från att upptäcka bruk av täcklös steganografi. Tidiga studier inom området behandlar bilder och text, senare studier har utökat området genom att behandla video och ljud. Den första studien inom täcklös ljudsteganografi publicerades år 2022. Målet med examensarbetet är att utöka forskningen med en föreslagen ljudkompatibel metod som använder tidsstämpeln då ett meddelande mottagits för att skapa en tidsbaserad nyckel som kan appliceras på en hash erhållen från ett steganografiskt medium. Detta tillåter mediet att representera olika hashar beroende på tiden, vilket ökar kapaciteten.   Syfte. Syftet med examensarbetet är att utforska vilka egenskaper i ett ljudmedia som lämpar sig åt steganografiskt bruk, att skapa en metod som kan hitta ljudklipp som representerar ett efterfrågat meddelande, samt att förbättra nuvarande state-of-the-art inom täcklös ljudsteganografi genom att finna en bra balans mellan kapacitet, robusthet och kostnad.   Metod. En litteraturstudie utfördes för att få förståelse för metoder använda i tidigare studier. Syftet var att hitta egenskaper i ljud som lämpar sig åt täcklös ljudsteganografi samt identifiera icke-täcklösa metoder som skulle kunna anpassas för att fungera som täcklösa. Experiment utfördes sedan på två dataset för att påvisa den ökning i effektiv kapacitet den föreslagna metoden ger när den appliceras på state-of-the-art-metoden inom täcklös ljudsteganografi. Experiment utfördes även för att utöka tidigare forskning på robustheten av state-of-the-art-metoden inom täcklös ljudsteganografi. Resultat. Resultaten visar att den föreslagna metoden kan öka kapaciteten per meddelande från åtta till 16 bits med 100% effektiv kapacitet med 200 nyckelpermutationer och en databas bestående av 50 stycken en-minut långa ljudklipp. De visar även att tidskostnaden för den föreslagna metoden är mindre än 0,1 sekund för 2048 nyckelpermutationer. Experimenten på robusthet visar att state-of-the-art-metoden har god robusthet mot additivt vitt gaussiskt brus, lågpassfilter och omsampling men är svagare mot kompression och bandpassfilter. Slutsatser. Vi fullbordar målen och utökar forskningen inom området genom att föreslå en metod kan öka kapaciteten av befintliga täcklösa metoder. Vi demonstrerar kapacitetsökningen genom att applicera vår metod på den senaste täcklösa ljudsteganografimetoden. Vi presenterar argument för vår metods tillämpning i områden utanför ljuddomänen och utanför metoden som den applicerades på. Slutligen diskuteras riktningar för framtida forskning.