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In Tunisia, one of the major problems of the olive oil industry is marketing. Several factors have an impact, such as quality, originality, lobbying, subsidies and the certification of extra virgin olive oil. The major problem remains the traceability of the production process to guarantee the origin of the food at all times. This fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that could bring visibility to the oil supply chain. It is proposed in order to guarantee the veracity of the product information at different stages. In this paper, a multi-Blockchain, multi-sensor traceability system using IoT will be presented. Two Blockchains that can be programmed via Smart Contract will be used. The first one is Quorum, which is a private Blockchain used by the actors of our system, and the second one is Ethereum, which is public and connects the different actors who have access to our system. This smart contract allows us to conta our system to track the olive oil manufacturing process from the farmer, through the oil mill, the transporter and the quality controller to the customer. A general approach for managing the olive oil supply chain is presented. This approach offers the possibility for the system to be configurable. It is based on smart contracts and applications that interact with the same smart contracts. The IoT is used to configure sensors. These sensors are the source of data for the supply chain process. These sensors are connected to the embedded platforms that host Quorum.

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.

Securing multimedia content and preventing it from being maliciously manipulated has developed at a rapid pace, and researchers have been studying the traitor tracing as an appropriate solution. This approach consists in retrieving back the actors who contributed to the construction of an illegal release of a multimedia product. It includes two major steps which are the fingerprinting step and the tracing one. The fingerprinting step relies on the watermarking technique whereas the efficiency of the tracing scheme depends on several requirements: the robustness of the watermarking technique, the type of the media content, and even the computational complexity. In this paper, we propose a new collusion-secure fingerprinting scheme for 3D videos. It has essentially a twofold purpose: at a first step, we propose to embed the watermark in the video copy by applying a standard Least Significant Bit (LSB) substitution to all the frames of both the 2D video and the depth map components in order to ensure simultaneously and independently the protection of these two parts. In the second step, we apply the tracing process whose target is the identification of eventual colluders by extracting the hidden identifier from the suspicious video and analyse it. Experimental assessments show that the proposed scheme provides interesting results in terms of speed and tracing accuracy constraints.

Due to the evolution of information technology, it is becoming increasingly easy to use new platforms in order to set up efficient systems that are well adapted to the expected needs. As part of improving security and facilitating the detection of potentially dangerous persons, an intelligent application for on-board facial recognition is being developed. It is within this framework that we propose this paper. The objective of the proposed work is twofold. On the one hand, we propose to develop a module for the detection of relevant facial characteristics, which is the first step of an intelligent video surveillance application. Based on the detection of points of interest of the Landmark algorithm, a software optimization of the work is proposed. On the other hand, this application will be decomposed in order to be embedded on a multiprocessor architecture. In order to validate the multiprocessor-based approach, a comparison with other existing powerful processor architectures will allow to validate the best approach. This work will be the input for an intelligent embedded face detection application based on Machine Learning.

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.

According to the ever development of multimedia distribution systems, more than one technique was proposed in the literature to address the copyright protection issue. One key technique was to propose a fingerprinting system based on traitor tracing process to retrieve back the traitorous users who can operate in the mid-way. Some previous works agree upon the fact that users belonging to the same group have more probability to collude together. Several researchers in the tracing traitor field agree upon the fact that constructing a group-based fingerprint should enhance the detection rates of the fingerprinting system. In this paper, we propose to generate a fingerprint having the group property by using a clustering algorithm. We propose to construct a group-based fingerprint according to a DCT-based audio watermarking technique which has proven good robustness and inaudibility results. To show the impact of the classifying algorithm, a set of experimental tests are conducted to check two relevant criteria: the capacity and the security of the group-based fingerprint.

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.

The encroachment of Internet and Peer to Peer networks has really facilitated our daily lives and works but it contributes to another dangerous phenomenon which is copying a digital content without having authorization, called piracy. To handle this phenomenon, several techniques of tracing traitors were proposed, by combining in the same time a fingerprinting technique to a watermarking one. In this paper, we first present basic notions for multimedia traceability framework: Anti Collusion code (ACC) and the watermarking technique. We show a study of available tracing traitors' schemes and we propose a comparison of accusation ability and computational costs of these techniques. Next we describe our future contribution in this target.

One of the most important issues of the digital watermarking is the watermark's robustness. That's why the error correcting codes (ECC) techniques were proposed. In this article, the performance of ECC in audio watermarking system is researched. The conclusion is that BCH encoders and turbo codes are the most important encoders. They have the best experimental robustness results against several audio signal attacks.