Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions

Network steganography conceals the transfer of sensitive information within unobtrusive data in computer networks. So-called micro protocols are communication protocols placed within the payload of a network steganographic transfer. They enrich this transfer with features such as reliability, dynamic overlay routing, or performance optimization --- just to mention a few. We present different design approaches for the embedding of hidden channels with micro protocols in digitized audio signals under consideration of different requirements. On the basis of experimental results, our design approaches are compared, and introduced into a protocol engineering approach for micro protocols.Comment: 20 pages, 7 figures, 4 table

Audio watermarking techniques using singular value decomposition

In an increasingly digital world, proving ownership of files is more and more difficult. For audio files, many schemes have been put into place to attempt to protect the rights of the digital content owners. In general, these techniques fall under the classification of Digital Rights Management (DRM). Audio watermarking is one of the less invasive schemes which embeds security into the data itself instead of in an outside layer meant to encapsulate and protect the data. There are many domains in which an audio watermark can be applied. The simplest is that of the time domain; often, however, other domains may be more desirable due to greater imperceptibility and robustness to attack. Common domains include the frequency domain, or domains similar to frequency through functions such as the Wavelet Transform. One domain of particular interest is that of the Singular Value Decomposition. The goal of this thesis is to propose and test many different watermarking schemes as well as test an existing watermarking scheme operating in the SVD domain in order to assess the viability of the SVD as a watermarking carrier domain. Different carrier matrices as well as bit embedding methods are explored. The use of a standard set of audio files was used to help test the systems; a standard set of watermarking tests was unavailable, so a comparable test bed was implemented and utilized

DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.Comment: Accepted by AAAI202

Virtual Eye – Revolutionizing Vision Assistance For People With Disabilities

Visually challenged individuals have faced numerous challenges in their daily lives. These challenges include: Visually challenged individuals have difficulty reading printed materials, including books, magazines, and newspapers. This limitation can significantly impact their education, as they may not have access to all the materials they need to learn. Moving around in unfamiliar places can be a daunting task for the visually impaired. They may struggle to access digital or printed materials, as these are often not available in accessible formats. It might be difficult for those who are blind to identify objects. This can be frustrating, especially in situations where they are alone and need to identify objects. To address this issue, we are developing a mobile application for visually challenged individuals by providing a range of features such as text-to-speech, speech-to-text, image-to-audio, and PDF-to-audio. It enables visually challenged individuals to access information, read books, identify objects, communicate, and navigate with ease and independence. The app's user-friendly interface can be accessed both manually and by voice command, making it easy to use for people with varying levels of technical expertise. Overall, the Virtual Eye app helps visually challenged individuals lead more fulfilling and independent lives. Overall, Virtual Eye application is an essential tool for visually challenged individuals, helping them navigate their daily lives with ease and independence. With this app, they can access information, communicate, and identify objects without the need for a third party, enhancing their quality of life and sense of autonomy