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A Brain-Computer Interface (BCI) is a system used to communicate with an external world through the brain activity. The brain activity is measured by electroencephalography (EEG) signal and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEG based brain-computer interface (BCI). The source localization of the human brain activities can be an important resource for the recognition of the cognitive state, medical disorders, and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets taken from 7 different wavelet families, which are applied to a Stationary Wavelet Transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, and reverse Biorthogonal wavelet families in extracting five different brainwave subbands for source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym20 mother wavelet as the best choice followed by bior6.8 and coif5.

Because of the availability of more than an actor and a wireless component among e-health applications, providing more security and safety is expected. Moreover, ensuring data confidentiality within different services becomes a key requirement. In this paper, we propose to collect data from health and fitness smart devices deployed in connection with the proposed IoT blockchain platform. The use of these devices helps us in extracting an amount of highly valuable heath data that are filtered, analyzed, and stored in electronic health records (EHRs). Different actors of the platform, coaches, patients, and doctors, collaborate to provide an on-time diagnosis and treatment for various diseases in an easy and cost-effective way. Our main purpose is to provide a distributed, secure, and authorized access to these sensitive data using the Ethereum blockchain technology. We have designed an integrated low-powered IoT blockchain platform for a healthcare application to store and review EHRs. This architecture, based on the blockchain Ethereum, includes a web and mobile application allowing the patient as well as the medical and paramedical staff to have a secure access to health information. The Ethereum node is implemented on an embedded platform, which should provide an efficient, flexible, and secure system despite the limited resources and low power consumption of the multiprocessor platform.

Advancement in information and communication technology in health sector has been developed at a rapid pace, and has consequently improved its different services. Securing patients’s health documents becomes crucial and researchers have investigated more than one privacy measures involving the access control, the security and the integrity of these documents. In this paper, we propose a novel QR-code-based watermarking technique for medical images. The generated QR code pattern is the result of converting a watermarked image which is obtained by embedding the concatenation of diagnostic results and a specific collusion-secure code to the original image.

The adoption of Internet of Things (IoT) technology across many applications, such as autonomous systems, communication, and healthcare, is driving the market’s growth at a positive rate. The emergence of advanced data analytics techniques such as blockchain for connected IoT devices has the potential to reduce the cost and increase in cloud platform adoption. Blockchain is a key technology for real-time IoT applications providing trust in distributed robotic systems running on embedded hardware without the need for certification authorities. There are many challenges in blockchain IoT applications such as the power consumption and the execution time. These specific constraints have to be carefully considered besides other constraints such as number of nodes and data security. In this paper, a novel approach is discussed based on hybrid HW/SW architecture and designed for Proof of Work (PoW) consensus which is the most used consensus mechanism in blockchain. The proposed architecture is validated using the Ethereum blockchain with the Keccak 256 and the field-programmable gate array (FPGA) ZedBoard development kit. This implementation shows improvement in execution time of 338% and minimizing power consumption of 255% compared to the use of Nvidia Maxwell GPUs.

The watermarking layer has a crucial role in a collusion-secure fingerprinting framework since the hidden information, or the identifier, directly attached to user identification, is implanted in the media as a watermark. In this paper, we propose a new zero watermarking technique for 3D videos based on Support Vector Machine (SVM) classifier. Hence, the proposed scheme consists of two major contributions. The first one is the protection of both the 2D video frames and the depth maps simultaneously and independently. Robust features are extracted from Temporally Informative Representative Images (TIRIs) of both the 2D video frames and depth maps to construct the master shares. Then, the relationship between the identifier and the extracted master shares is generated by performing an Exclusive OR (XOR) operation. The second contribution uses the SVM and the XOR operation to estimate the watermark. Compared to other zero watermarking techniques, the proposed scheme has proven good results of robustness and transparency even for long size watermarks, which makes it suitable for a tracing traitor framework.

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).

Developing multimedia embedded applications continues to flourish. In fact, a biometric facial recognition system can be used not only on PCs abut also in embedded systems, it is a potential enhancer to meet security and surveillance needs. The analysis of facial recognition consists offoursteps: face analysis, face expressions’ recognition, missing data completion and full face recognition. This paper proposes a hardware architecture based on an adaptation approach foran algorithm which has proven good face detection and recognition in 3D space. The proposed application was tested using a co design technique based on a mixed Hardware Software architecture: the FPGA platform.

The easiness of using and manipulating digital media content has a volte face. In fact, although average users can simply be familiar with some manipulations such as a simple duplication, these manipulations can be dangerous with dishonest users whose target is illegal. Manipulating and duplicating digital media content via the Internet and Peer to Peer networks is available even to average users but can be used to unauthorized purposes with dishonest customers. Henceforth, facing the loss caused by unauthorized treatments and protecting the digital content become challenging to the media industry and research has led to different mechanisms of digital content protection. The aim of the multimedia distribution platforms, even Video on demand platforms, is to propose a suitable structure to the embedded fingerprints to ensure an efficient and fast tracing process in multimedia distribution platforms involving great number of users. The Tardos code has been the most popular tracing code due to its efficient tracing detection performance. One main challenge of the existing Tardos-based tracing approaches was to face the decoding complexity and the computational costs of the tracing process.

International audience; The multimedia traitor tracing field involves the embedding of a collusion secure fingerprint in the host signal to retrieve and prevent any multimedia content fraud. Trendy work aims at providing a tracing system which offers a good protection of the digital content and an efficient tracing process. These challenges depend on reducing the length of the embedded fingerprint and the complexity of the accusation process. Furthermore, addressing these issues becomes more and more relevant in media distribution applications involving an important number of users. In this paper, we propose a secure fingerprinting system based on a two-stage tracing strategy which combines two probabilistic tracing codes: Boneh Shaw with replication scheme and Tardos codes. This strategy is applied to a multilevel hierarchical fingerprint which is then embedded using a DCT-based audio watermarking technique. By taking the advantage of grouping users and applying a weight-based tracing process, the proposed fingerprinting system offers to reduce efficiently the computational costs of the tracing time. It has also a good robustness to different types of attacks. We have carried out different tests to evaluate the performance of the system in terms of robustness and imperceptibility. The experimental results show that the proposed fingerprinting system provides a suitable solution to the the infringement copyright problem in multimedia distribution platforms by reducing significantly the users’ retrieval space and performing good detection results in a reduced time.

In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The Expectation Maximization algorithm has shown to be an efficient solution to estimate the collusion channel and thus to tune the Tardos accusation functions. In this paper, we explore the impact of this algorithm in a group-based tracing scheme to deal with the computational costs and the invariance of the Tardos accusation performance. The tracing scheme we propose benefits from a twofold accusation process. Indeed, in a first time, it is based on a two-level tracing strategy which consists in tracing guilty groups in a first level with the Boneh Shaw tracing code and in retrieving at least one colluder in accused groups with Tardos code in the second level. This strategy has reduced efficiently the decoding process of the Tardos code. The main shift we propose in the second level is to apply the Expectation Maximization algorithm to be tightly tied to collusion yielded by colluders and hence to find the more accurate Tardos accusation functions. The performance of the resulting tracing scheme is evaluated according to different criteria and promising results have been achieved when compared to the existing tracing schemes proposed in the literature.

International audience; One of the key challenges of a traitor tracing scheme is to deal with the real scenarios. In this context, the tracing operation is usually constrained by the lack of information about the number of colluders and even the collusion channel. Indeed, the Tardos decoding is invariant regardless the type of collusion, which can be considered as a suboptimality of its accusation performance. In this paper, we propose to use a MAP-based estimation strategy which improves the Tardos decoding step and guarantees a good estimation results. Compared to the original version of Tardos code and the original MAP decoder operating in blind tracing scheme, the proposed technique takes the advantage of operating in hierarchical context to provide a more concise and accurate accusation decision, this in a short time.

International audience; Handling a great number of users and surviving different types of attacks present fundamental challenges of the major- ity fingerprinting systems in the tracing traitor field. In this paper, the proposed technique consists in embedding a finger- print, a QR code in the audio stream extracted from the media release. Using the QR-code provides several advantages as supporting a large amount of information in a compact for- mat end damage resiliency. This paper proposes to encode the identifier which is a parallel concatenation of two tracing codes: Boneh Shaw and Tardos codes into QR-code. The proposed approach should not only improve the two-stage tracing strategy by reducing the complexity computation, but also enhance the secure side of the proposed technique by the preprocessing treatment before generating the QR-code.