Expressive Color Visual Secret Sharing with Color to Gray & Back and Cosine Transform

Color Visual Secret Sharing (VSS) is an essential form of VSS. It is so because nowadays, most people like to share visual data as a color image. There are color VSS schemes capable of dealing with halftone color images or color images with selected colors, and some dealing with natural color images, which generate low quality of recovered secret. The proposed scheme deals with a color image in the RGB domain and generates gray shares for color images using color to gray and back through compression. These shares are encrypted into an innocent-looking gray cover image using a Discrete Cosine Transform (DCT) to make meaningful shares. Reconstruct a high-quality color image through the gray shares extracted from an innocent-looking gray cover image. Thus, using lower bandwidth for transmission and less storage

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques

Algorithms and Architectures for Secure Embedded Multimedia Systems

Embedded multimedia systems provide real-time video support for applications in entertainment (mobile phones, internet video websites), defense (video-surveillance and tracking) and public-domain (tele-medicine, remote and distant learning, traffic monitoring and management). With the widespread deployment of such real-time embedded systems, there has been an increasing concern over the security and authentication of concerned multimedia data. While several (software) algorithms and hardware architectures have been proposed in the research literature to support multimedia security, these fail to address embedded applications whose performance specifications have tighter constraints on computational power and available hardware resources. The goals of this dissertation research are two fold: 1. To develop novel algorithms for joint video compression and encryption. The proposed algorithms reduce the computational requirements of multimedia encryption algorithms. We propose an approach that uses the compression parameters instead of compressed bitstream for video encryption. 2. Hardware acceleration of proposed algorithms over reconfigurable computing platforms such as FPGA and over VLSI circuits. We use signal processing knowledge to make the algorithms suitable for hardware optimizations and try to reduce the critical path of circuits using hardware-specific optimizations. The proposed algorithms ensures a considerable level of security for low-power embedded systems such as portable video players and surveillance cameras. These schemes have zero or little compression losses and preserve the desired properties of compressed bitstream in encrypted bitstream to ensure secure and scalable transmission of videos over heterogeneous networks. They also support indexing, search and retrieval in secure multimedia digital libraries. This property is crucial not only for police and armed forces to retrieve information about a suspect from a large video database of surveillance feeds, but extremely helpful for data centers (such as those used by youtube, aol and metacafe) in reducing the computation cost in search and retrieval of desired videos

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

De-identification for privacy protection in multimedia content : A survey

This document is the Accepted Manuscript version of the following article: Slobodan Ribaric, Aladdin Ariyaeeinia, and Nikola Pavesic, ‘De-identification for privacy protection in multimedia content: A survey’, Signal Processing: Image Communication, Vol. 47, pp. 131-151, September 2016, doi: https://doi.org/10.1016/j.image.2016.05.020. This manuscript version is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License CC BY NC-ND 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents.Peer reviewe

Tratamiento óptimo de contaminantes y sistemáticos para la explotación presente y futura de datos del Fondo Cósmico de Microondas

Uno de los hitos más esperados en cosmología es la detección de las ondas gravitacionales primordiales, ya que constituirían una prueba irrefutable de la existencia de un periodo inflacionario. En principio, pueden medirse a través de la huella marcada en la señal del modo B del Fondo Cósmico de Microondas. Sin embargo, esta detección conlleva muchos retos desde el punto de vista experimental y de análisis de datos, ya que es relativamente débil en comparación con otras fuentes de modos B, como los contaminantes astrofísicos, los modos lensados de E a B, y los errores sistemáticos. Esta tesis es uno de los muchos esfuerzos en el campo del análisis de datos dedicados a la detección de esta señal de forma insesgada. Este trabajo es una tesis por compendio de artículos que incluye cuatro estudios realizados en el contexto de la separación de componentes aplicada a los datos de polarización del Fondo Cósmico de Microondas. Se presenta un nuevo método de separación de componentes (B-SeCRET) que se ha aplicado en varios contextos dentro de diferentes colaboraciones e iniciativas como: simulaciones para estudios predictivos de la futura misión del satélite LiteBIRD, simulaciones de iniciativas de experimentos en tierra como ELFS, y a los datos del instrumento QUIJOTE MFI, WMAP y Planck. Además se incluyen tres aplicaciones de esta metodología: 1) el estudio y optimización de los diseños experimentales, 2) la mitigación de los errores sistemáticos, y 3) la caracterización de contaminantes astrofísicos. En particular se probó: 1) la viabilidad de la detección de ondas gravitaciones con un telescopio terrestre operando el régimen de microondas de baja frecuencia (de 10 a 120 GHz), además de su alta complementariedad con otras misiones como LiteBIRD, 2) la posibilidad de calibrar los ángulos de polarización a partir de la señal multifrecuencia mediante dos métodos (uno basado en anular el cross espectro de potencias EB y otro parametrizando este sistemático en la parte de separación de componentes con B-SeCRET), ambas metodologías recuperan una estimación insesgada de la amplitud de las ondas gravitacionales primordiales, 3) la mejora en la caracterización de la emisión de sincrotrón cuando se añaden los datos del instrumento QUIJOTE-MFI junto con datos de WMAP y Planck, en particular se presenta el primer mapa detallado del índice espectral del sincrotrón en hemisferio norte el cual presenta variaciones espaciales más significativas que los obtenidos con solo datos de WMAP y Planck. En conclusión, esta tesis prueba que B-SeCRET es una metodología versátil para analizar los datos presentes y futuros relativos al estudio del fondo cósmico de microondas debido a su capacidad de tratar simultáneamente con contaminantes astrofísicos y errores sistemáticos.One of the most awaited milestones in cosmology is the detection of primordial gravitational waves, as they would constitute compelling evidence of the existence of an inflationary period. In principle, they can be measured through their imprint in the B-mode signal of the Cosmic Microwave Background. However, this detection carries many challenges from an experimental and data analysis point of view, as it is relatively weak compared to other sources of B-modes, such as astrophysical contaminants, E-to-B lens modes, and systematic errors. This thesis is one of many efforts in the field of data analysis devoted to the detection of this signal in an unbiased manner. This work is a compilation thesis that includes four studies performed in the context of component separation applied to Cosmic Microwave Background polarization data. A new component separation method (B-SeCRET) is presented. This method has been applied in several contexts within different collaborations and initiatives, such as simulations for predictive studies of the future LiteBIRD satellite mission, simulations of ground-based experiment initiatives such as ELFS, and QUIJOTE MFI, WMAP and Planck instrument data. In addition, three applications of this methodology are included: 1) the study and optimization of experimental designs, 2) the mitigation of systematic errors, and 3) the characterization of astrophysical contaminants. In particular, we tested: 1) the feasibility of gravitational wave detection with a ground-based telescope operating the low-frequency microwave regime (from 10 to 120 GHz), in addition to its high complementarity with other missions such as LiteBIRD, 2) the possibility of calibrating the polarization angles from the multi-frequency signal using two methods (one based on canceling the EB cross-spectrum and the other by parameterizing this systematic in the component separation part with B-SeCRET), both methodologies recover an unbiased estimate of the amplitude of the primordial gravitational waves, 3) the improvement in the characterization of the synchrotron emission when the QUIJOTE-MFI instrument data are added together with WMAP and Planck data, in particular, the first detailed map of the synchrotron spectral index in the northern hemisphere is presented, which presents more significant spatial variations than those obtained with only WMAP and Planck data. In conclusion, this thesis proves that B-SeCRET is a versatile methodology to analyze present and future data related to the study of the cosmic microwave background due to its ability to deal simultaneously with astrophysical contaminants and systematic errors

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.