Currently, most transactions and exchanges are conducted through the Internet thanks to technological tools, running the risk of the falsification and distortion of information. This is due to the massive demand for the virtual world and its easy access to anyone. Image watermarking has recently emerged as one of the most important areas for protecting content and enhancing durability and resistance to these kinds of attacks. However, there is currently no integrated technology able to repel all possible kinds of attacks; the main objective of each technology remains limited to specific types of applications, meaning there are multiple opportunities to contribute to the development of this field. Recently, the image watermarking field has gained significant benefits from the sudden popularity of deep learning and its outstanding success in the field of information security. Thus, in this article, we will describe the bridge by which the watermarking field has evolved from traditional technology to intelligent technologies based on deep learning.

The use of thermography in the early diagnosis of Diabetic Foot (DF) has proven its effectiveness in identifying areas of the plantar foot that are susceptible to ulcer development. Segmentation of the foot sole is one of the most pertinent technical issues that must be performed with great precision. However, because of the inherent difficulties of foot thermal images, such as unclarity and the existence of ambiguities, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical use. In this study, we aim to develop a fully automated, robust and accurate segmentation of the diabetic foot. To this end, we propose a deep neural network architecture adopting the encoder-decoder concept called Double Encoder-ResUnet (DE-ResUnet). This network combines the strengths of residual network and U-Net architecture. Moreover, it takes advantage of RGB (Red, Green, Blue) colour images and fuses thermal and colour information to improve segmentation accuracy. Our database consists of 398 pairs of thermal and RGB images. The population includes two groups. The first group of 54 healthy subjects. And a second group of 145 diabetic patients from the National Hospital Dos de Mayo in Peru. The dataset is splitted into 50% for training, 25% for validation and the last 25% is used for testing. This proposed model provided robust and accurate automatic segmentations of the DF and outperformed other state of the art methods with an average intersection over union (IoU) of 97%. In addition, it is able to accurately delineate the part of toes and heels which are high risk regions for ulceration.

Recently, using videos as a learning resource has received a lot of attention and turned widely exploited as an effective learning tool. With the rapid spread of instructional videos, the number of these tools in all disciplines is becoming very high. It is fractious for learners to find video courses adapted to their needs. Recommender system is an important tool; it can help learners for better navigation and better guidance to access and reach information and interesting videos content and restrict the number of choices provided. This article presents results of a systematic literature review on video-based learning recommender systems. This systematic literature review included the papers indexed in five scientific digital libraries and published between 2009 and 2023. Our review focuses on examining both the technical and pedagogical aspects of video-based learning recommender systems. In terms of technical aspects, we analyze the most commonly used recommendation approaches in video-based learning, as well as additional methods and techniques employed to enhance the effectiveness of video-based learning recommendations. We also explore the variables and data sources that have been investigated in the process of recommending videos for learning purposes, along with the evaluation metrics used to assess the performance of these systems. Regarding the pedagogical aspects, we investigate the learning environments where these systems have been implemented, the subjects and educational programs that hold primary importance in video-based learning recommendations, and the overall learning process.

Watermark robustness against both geometric and signal processing attacks is still challenging in the watermarking community. In this work, we propose an approach to increase the watermark detection rate. The novel feature of the method resides in pre-processing the image before the embedding process, and involves no interaction with other improvements. It is based on the following idea: given the same watermarking scheme, some images are naturally more appropriate for the embedding process as they obtain better detection rates than others. In the Fourier scheme for example, it was observed that lower amplitude variations at Fourier coefficients correspond to a better detection rate. We theoretically justify these findings and demonstrate that by applying a dedicated low-pass filtering on the magnitude of the frequency bands the watermark detection rate is significantly improved.

Digital image watermarking is an active research field since it provides protection, security, and authenticity of data. This paper presents development and implementation of a blind and robust watermarking application for ID images under a print-cam system. In the present case, the images are watermarked and printed on ID cards and then detected freehandedly with a smartphone camera. In order to design an efficient and robust image watermarking application, the attacks produced in print-cam processes, such as geometric distortions, must be resolved. Accordingly, the proposed watermarking approach is applied in the Fourier domain. Then, a frame-based projective rectification is integrated to deal with geometric distortions by using detection of Hough lines. Moreover, better robustness against print-cam watermarking attacks was achieved compared with the existing methods, and an Android application was designed and implemented based on the proposed scheme.