International audience

Today, the popularity of 3D videos is increasing significantly. This trend can be attributed to their immersive appeal and lifelike experience. In an era dominated by the widespread distribution of digital content, data integrity, and ownership, all of these elements are of crucial importance. In this context, the practice of traitor tracing, closely related to Digital Rights Management (DRM), facilitates the identification and tracking of unauthorized users who have violated copyright in order to share illegal copyright-protected content. In this paper, we propose a solution to this problem, we introduce an innovative traitor tracing approach focused on 3D video, with a particular focus on the DIBR (Depth Image-Based Rendering) format, which can be vulnerable to an Interleaving attack strategy. For this purpose, we develop a new phylogeny tree construction method designed to combat collusion attacks. Our experimental evaluations demonstrate the effectiveness of our proposed approach particularly when applied to long fingerprinting codes. Compared to Tardos’ approach, our method delivers very good results, even for a large number of colluders.

In multimedia forensics, most research focuses on analyzing individual assets, such as images or videos, to verify their authenticity by analyzing their intrinsic properties. However, provenance analysis examines multiple assets collectively, tracing their histories through pairwise relationships. This approach is beneficial in identifying traces of manipulation and understanding the evolution of digital content. In this research, we apply provenance analysis to detect and track malicious users engaged in collusion attacks by analyzing their fingerprinting codes. Specifically, the correlations we consider are not generic media content similarities, but rather statistical and structural relationships between the embedded fingerprinting codes of different multimedia assets. By quantifying these correlations, our method can reliably identify individuals responsible for generating unauthorized copies and distinguish between different collusion strategies. Furthermore, it allows us to classify and estimate the specific type of collusion attack employed, providing deeper insights into the strategies used by attackers, thereby aiding in the development of more robust countermeasures and enhancing the resilience of multimedia security systems.

Federated Learning can ensure privacy by design through its unique approach to data analysis and model training. Adapting Machine Learning models into federated architectures is particularly interesting, especially in the context of anomaly detection and data preservation in time series data of smart meters. In this study, we explore Machine Learning models deployed and depicted on an open dataset to demonstrate the efficiency of Machine Learning based Federated Learning frameworks in preserving privacy. Through comprehensive experiments and evaluations, we highlight the significant privacy achieved through decentralized data, local model training and aggregated model updates. By harnessing the power of the federated learning, we offer a promising solution for preserving privacy in anomaly detection while enabling effective analysis of smart meter data.

In the midst of crisis of loss of trust in the world, the Blockchain technology has emerged as a suitable solution. It is considered as a revolutionary technology that is able to solve complex problems in different fields involving trust and disintermediation. In this article, we are interested in finding optimized solutions for parking spaces' target. Hence, we present an intelligent parking system managed by a decentralized system based on the Blockchain, which brings major changes to facilitate the reservation of vehicles.

Patient medical data is the key data of an e-health application. Medical data frequently play an important role in disease analysis and are implicated in motivating values for educating diagnostic methods and modifying diagnostic results. Confidentiality of patient health documents is more important, and therefore researchers have considered many security measures, including access control, confidentiality, and integrity of these documents. To solve the security problem, this paper presents an advanced solution based on converting medical data into compact size, in the form of QR code before being deployed on our Blockchain. In this way, performing complex operations will be safely performed off-chain, such as collecting and preprocessing patient data, while submission of this data is done on an hourly basis. Chain. We recommend a data flow architecture that combines the Internet of Things (IoT) with blockchain (especially the Ethereum architecture), using smart contracts to access and store data while keeping Strong against many attacks. Our proposed system is effective in sharing and managing patient electronic health data. The tests involved implementing Ethereum chain configurations on PCs and two embedded IoT platforms, namely Raspberry Pi and PYNQ cards. Performance is evaluated in terms of security, execution time, memory, power and CPU consumption of various patient data scenarios.

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.

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.

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.

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.