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Se ha desarrollado un Agente Robótico capaz de aprender del entorno dinámico por el que navega, el cual tiene como objetivo hallar un objeto específico. Para el crecimiento de su aprendizaje, se ha creado la Neuroevolución de Redes Neuronales Híbridas en un Agente Robótico (NRNH-AR) que combina redes como la CNN para entender el entorno y ANN para realizar acciones, esto se complementa con el Aprendizaje por Refuerzo Profundo y la Política de Gradiente. Sin embargo, para que el algoritmo tenga éxito prácticamente en un robot físico, se han considerado además dos bloques: el Hardware y la mecánica involucrada, pues se entrenará de forma on-line para evitar problemas de latencia y limitación de ancho de banda. Con esta investigación, se ha demostrado que con la NRNH-AR es posible implementar el Aprendizaje por Refuerzo Profundo dentro de un Robot, optimizando en tiempo y costo computacional teniendo un aprendizaje evolutivo.

La necesidad de reducir el ruido ambiental incrementa en la misma proporción que aumenta el número de aparatos eléctricos tanto domésticos como industriales. Para resolver este problema se han desarrollado diversos esquemas activos de cancelación de ruido basados en filtros adaptables, los cuales generan una réplica de la señal de ruido con fase inversa de manera que el ruido se cancela por superposición de dos ondas acústicas, siendo capaces de seguir las variaciones en las características de las señales de ruido. Estos esquemas llevan a cabo la cancelación en el dominio acústico en lugar de hacerlo en el dominio eléctrico por lo cual deben operar en un espacio tridimensional. Este artículo presenta el desarrollo de un sistema de cancelación acústica de ruido el cual permitirá la cancelación de ruido en un ducto, empleando filtros adaptables de respuesta finita tanto para estimar las señales de ruido, como la trayectoria secundaria. Los resultados experimentales tanto en el dominio del tiempo como de la frecuencia muestran que esquema propuesto proporciona una atenuación de al menos 20dB operando con señales no estacionarias como lo son ruido producidos por motores ya sea de camión o avión entre otros.

Uno de los factores importantes que influyen en los accidentes automovilísticos es el manejar bajo condiciones no-óptimas, tales como estrés, ira, miedo, depresión entre otros, en las cuales la posibilidad de sufrir un accidente durante manejo se incrementa. Por lo tanto, hasta la fecha han sido propuestos varios esquemas que detectan la emoción del conductor basado en su expresión facial. La mayoría de ellos usan solo un fotograma (una imagen) y operan en condiciones restringidas que rara vez se presentan en condiciones reales de manejo. Con el fin de poder resolver este problema, este artículo presenta un algoritmo para el reconocimiento de la emoción del conductor basado en sus expresiones faciales, en el cual a partir de la secuencia de cuadros de video se extraen vectores de características temporales usando los puntos relevantes del rostro. Los vectores de características extraídos se introducen en diferentes clasificadores, tales como Maquina de Soporte Vectorial (SVM) y K-vecinos más cercanos (KNN) para la comparación de su funcionamiento.

Visible watermarking offers direct copyright protection to the naked eye using visible pattern superimposed in the host image. However, due to their exposure, the watermark pattern becomes an easy target to remove by malicious entities that generates copyright-free images. Until now, in the scientific literature several semi-automatic visible watermark removal attacks have been proposed, showing enough visual quality of the recovered host image. In this paper, we propose a watermark design based on modified Seam Carving technique to provide robustness against removal attacks including inpainting-based attacks, Independent Component Analysis (ICA)-based attacks, and image matting-based attacks. Robustness against the attacks is measured using common image quality assessments and a watermark visibility assessment based on the Human Visual System (HVS). Experimental results show robustness of the proposed method against above mentioned watermark removal attacks under the most severe situation, in which adversary has the logotype used as a watermark pattern because it is public in Internet. The proposed method can be applied to any visible watermarking algorithms to make them robust against illegal removal attempts, without sacrificing their original functionality, such as reversibility for authorized users.

Cryptographic algorithms have been widely used in all secure data transmissions due their capabilities to hide sensible information against eavesdroppers. Nevertheless, technological progress tends to develop new cryptographic methods based on non-classical computing paradigms. In this work, the first circuit of visual cryptography based on Spiking Neural P systems (VCSN P) is presented. The proposed circuit makes use of new biological behaviors in astrocytes, which are involved in processes of plasticity and pruning control in dendrites, that can be the beginning to new cryptographic algorithms based on SN P systems. To validate the SN P circuit, this was implemented on the Cyclone V field programmable gate array (FPGA) development kit. The results demonstrate that this new approach exploits the features of the SN P systems to improve the cryptographic algorithm.

Medical information management has progressed in the last few years because of the advances in information technologies. Nowadays, it is possible to share medical images among specialists geographically distant to interpret, discuss, and get improved diagnostics. However, any alteration of transmitted image metadata may lead to issues related to information security, such as detachment and authentication. Detachment refers to link the data of an electronic patient record to an incorrect medical image, while authentication aims to identify the image source. These security problems are critical as they may cause the loss of sensitive data or wrong medical diagnoses. Digital watermarking is an emerging technique that faces these security problems as it allows to embed the metadata directly into the medical image. This paper proposes a hybrid and robust watermarking technique to prevent detachment and authenticate medical images. The quantization index modulation algorithm under dither modulation in conjunction with forwarding error correction is used to embed relevant metadata as a robust-imperceptible watermarking to avoid detachment. The visible-imperceptible watermarking paradigm, whose use is an innovation in medical images, is applied to insert a second watermark in the spatial domain to perform authentication. The experimental results show the contribution of the proposed scheme and its efficiency regarding robustness and imperceptibility.

Considering that an accurate detection of infected mosquitos may directly avoid the propagation of mosquito-borne disease; in this paper, we propose a detection system of infected mosquitos by Dengue virus type II, that uses seven spectral feature measures, which are applied to the spectrogram estimated from wingbeat signal emitted by mosquito’s flight. To evaluate the proposed system, we construct our own dataset with 20 infected Aedes aegypti by Dengue and 20 healthy ones. Seven spectral analysis methods, such as Spectral Rolloff, Spectral Centroide, etc., are applied to the spectrogram obtained by using the Short Time Fourier Transform (STFT) to generate feature vectors with 15 elements. These are feed into common machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression to detect the infected mosquitos differentiating form the healthy ones. Evaluation results show that, the best detection accuracy (84.32%) is provided by the KNN with K=3.

Drowsiness and driver distraction are considered the main causes of traffic accidents in the world. Considering this situation, this paper proposes two important modifications to our previously proposed driver drowsiness and distraction detector for real-time implementation on handheld mobile devices, such as smartphones. The first modification is due to a large variation in the capacity of mobile devices. To adapt the proposed system to a wide range of mobile devices, we present two automatic threshold calculations, which are used to differentiate driver drowsiness from normal blinking and dangerous driver distraction from normal short-term distraction. The second modification is related to the alarm during a continuous dangerous situation of the driver. We introduce a new algorithm to ensure the continuous activation of the alarm while the dangerous situation continues. These improvements perform as the general algorithm, since when it was implemented in mobile devices with low computational power, as well as in devices that do not have these limitations, the alarm activation times were not affected; On the other hand, it was possible to increase the accuracy originally given by the first system with respect to Ground Truth by almost 25% on average, resulting in alarm activations not being affected to a great extent by the natural errors that the convolutional neural networks (CNN) may cause, these improvements are shown and supported by the implementation in real time through video links provided in this work.

In this paper, we propose a portable device named SOMN_IA, to detect drowsiness and distraction in drivers. The SOMN_IA can be installed inside of any type of vehicle, and it operates in real time, alerting the dangerous state caused by drowsiness and/or distraction in the driver. The SOMN_IA contains three types of alarm: light alarm, sound alarm, and the transmission of information about the driver’s dangerous state to a third party if the driver does not correct his/her dangerous state. The SOMN_IA contains a face detector and a classifier based on the convolutional neural networks (CNN), and it aids in the management of consecutive information, including isolated error correction mechanisms. All of the algorithmic parts of the SOMN_IA are analyzed and adjusted to operate in real-time in a portable device with limited computational power and memory space. The SOMN_IA requires only a buck-type converter to connect to the car battery. The SONM_IA discriminates correctly between real drowsiness and normal blinking, as well as between real dangerous distraction and a driver’s normal attention to his/her right and left. Although the real performance of the SOMN_IA is superior to the CNN classification accuracy thanks to isolated error correction, we compare the CNN classification accuracy with the previous systems.

Human health is threatened by several diseases for this reason automated medical diagnosis systems has been developed several years ago. These systems need databases, the creation of these databases is tedious, arduous and stops being done so the created database is incomplete or unbalanced. Sometimes the databases are private to protect the private information of the patients, among other problems. For this reason, the researchers have started to use synthetic data. The synthetic data have been applied by different hospitals in the USA. The creation of synthetic data has different problems like the synthetic data are generated using rules defined by the user, the proposed approaches only can create one kind of data, the proposals require input from domain experts, among others. To address these kinds of problems, we propose a novel approach, which consists of the Bidirectional Recurrent Neural Network and the statistical stage to generate synthetic biomedical signals. The approach is able to create 5 kinds of biomedical signals (ECG, EEG, BCG, PPG, and Respiratory Impedance). Our approach is able to create synthetic data for patients or for specific events. The performance of our approach is compared with other generative models (GAN’s) through evaluation metrics. The created synthetic data are used to construct models; these models are able to successfully differentiate between different signals with high accuracies.

Nowadays, the administration of medical images can be compromised by several information security issues. Among others, a first problem consists in authenticating the source that originated the medical image and verifying that it corresponds to the correct patient; a second problem is aimed to avoid detachment between an electronic patient record and its corresponding images. A promising solution is the use of digital watermarking techniques applied to medical images considering in the design requirements of imperceptibility-robustness of the field of medical imaging. In this paper a digital watermarking method for an efficient administration of ultrasound images is proposed. To avoid the detachment between the record and images, we use an invisible watermarking in the frequency domain of the image. To authenticate the source of origin of the medical image, we use a visible-imperceptible watermarking in the spatial domain of the image. The experimental results show an efficient robustness and imperceptibility of the embedded watermark signals into the medical image. A comparison of performance with related works previously reported in the literature is included.

Heart disease is the principal cause of death across the globe and the ECG signals are used to diagnose it. The correct classification of this disease allows us the opportunity to apply a more focused treatment. ECG signals are fed into Automated Diagnosis Systems, and these systems use techniques like processing digital signals, machine learning, and deep learning. This paper shows the results when the sampling frequency of the ECG signals is resampled and proposes a new preprocessing stage. The new stage applies Wavelet based on Atomic Functions to eliminate the noise and baseline wander. The Wavelet based on Atomic Functions have demonstrated successful performances in computer science. The ECG signals are segmented into 1, 2, 5, and 10 s; these segmented signals are fed into the classifier stage. Our proposal was tested in four accessible public databases separately, and finally by gathering the databases. We were able to successfully differentiate between 11 types of ECG signals with an accuracy of 98.9%.

One of the most significant current discussions in Computer Science is the skin recognition. Many papers have studied the skin detection using different techniques, artificial vision, machine learning, deep learning among others. Despite its long success, the skin recognition has several problems in use. However, there has been little discussion about generate a skin tonalities classification. The aim of this paper is to propose a system to skin tonalities through geographic distribution based on clustering algorithms, pattern recognition and fuzzy logic. This distribution gives us the opportunity to classify the skin tonalities. We can study each skin tonality for any applications as medical diagnosis, security. In the first stage, we use the RGB color model to training the system. Then, we tested the system with different color models. We use color model with the best result to propose geographic distribution based skin tonalities. The results show that is possible to generate a skin tonalities classification. The proposed system is using to skin recognition, showing interesting results under controlled and no controlled conditions.

The development of acoustic scenes recognition systems has been a topic of extensive research due to its applications in several fields of science and engineering. This paper proposes an environmental system in which firstly a time-frequency representation is obtained using the Continuous Wavelet Transform (CWT). The time frequency representation is then represented as a color image using the Viridis color map, which is then inserted into a Deep Neural Network (DNN) to carry out the classification task. Evaluation results using several public data bases show that proposed scheme provides a classification performance better than the performance provided by other previously proposed schemes.

In recent years, the reversible data hiding techniques also known as lossless or invertible data hiding, has gradually become a very active research area. The reversibility of these schemes makes possible to extract the embedded data without errors, as well as to restore the cover medium to its original state. Furthermore, to guarantee the security and confidentiality of the hidden data and the image, reversible data hiding schemes over encrypted domain are presented as a promising solution to solve several issues of information security. This paper presents a study case of reversible data hiding schemes over encrypted domain oriented to the protection of the sharing and distribution of color images hosted in cloud storage services. The experimental results are presented in terms of imperceptibility, capacity, confidentiality, and visual quality, respectively.

This paper proposes the use of the FASSD-Net model for semantic segmentation of human silhouettes, these silhouettes can later be used in various applications that require specific characteristics of human interaction observed in video sequences for the understanding of human activities or for human identification. These applications are classified as high-level task semantic understanding. Since semantic segmentation is presented as one solution for human silhouette extraction, it is concluded that convolutional neural networks (CNN) have a clear advantage over traditional methods for computer vision, based on their ability to learn the representations of appropriate characteristics for the task of segmentation. In this work, the FASSD-Net model is used as a novel proposal that promises real-time segmentation in high-resolution images exceeding 20 FPS. To evaluate the proposed scheme, we use the Cityscapes database, which consists of sundry scenarios that represent human interaction with its environment (these scenarios show the semantic segmentation of people, difficult to solve, that favors the evaluation of our proposal), To adapt the FASSD-Net model to human silhouette semantic segmentation, the indexes of the 19 classes traditionally proposed for Cityscapes were modified, leaving only two labels: One for the class of interest labeled as person and one for the background. The Cityscapes database includes the category “human” composed for “rider” and “person” classes, in which the rider class contains incomplete human silhouettes due to self-occlusions for the activity or transport used. For this reason, we only train the model using the person class rather than human category. The implementation of the FASSD-Net model with only two classes shows promising results in both a qualitative and quantitative manner for the segmentation of human silhouettes.

espanolEste articulo propone un esquema en el cual inicialmente se obtiene una representacion tiempo-frecuencia usando la transformada wavelet continua (CWT), la cual tiene una resolucion logaritmica en el plano de la frecuencia similar a la del sistema auditivo humano. El desarrollo de este tipo de sistemas para la clasificacion de sonidos ambientales ha sido un topico de amplia investigacion debido a sus aplicaciones en diversos campos de la ciencia e ingenieria. Al igual que otros esquemas de clasificacion, estos se basan en la extraccion de parametros caracteristicos, los cuales se insertan en la etapa de clasificacion. La CWT se inserta en una red neuronal profunda, para llevar a cabo el proceso de clasificacion. Los resultados obtenidos, usando bases de datos de sonidos ambientales tales como, ESC-50, TUT Acoustic Scene y SONAM-50, demuestran que el esquema propuesto proporciona un funcionamiento superior al de otros esquemas previamente propuestos. EnglishThis paper proposes a scheme in which a time-frequency representation is first obtained using the continuous wavelet transform (CWT), which has a logarithmic resolution in the frequency domain, like that of the human ear. The development of these environmental sound classification systems is a topic of extensive research due to its application in several fields of science and engineering. Like other classification schemes, they are based on the extraction of specific parameters that are inserted in the classification stage. The CWT is then inserted into a deep learning neural network to carry out the classification task. The evaluation results obtained using several databases such as ESC-50, TUT Acoustic Scene, and SONAM-50 show that the proposed scheme provides a classification performance that is better than that provided by other previously proposed schemes.

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

This paper proposes a new symmetric encryption system based on an elliptical curve and chaos, where the encryption is done in a single block and runs for 14 rounds. Here, the 15 encryption keys have the same size as the image and are generated using a solution point of a strong elliptic curve. Using a string of random numbers obtained with a logistic map, a permutation and its inverse are generated, which improve the encryption level and add diffusion to the cryptosystem. Another important contribution to this research is the generation of a substitution box with a non-linearity of 100, which strengthens the cryptosystem against differential and linear attacks that add confusion to the scheme. Moreover, the cryptographic properties of the proposed S-Box are compared with those of the S-Box of the Advanced Encryption Standard (AES) to ascertain that it is a suitable alternative that it is resistant to differential power analysis (DPA) attacks. To verify the robustness of proposed cryptosystem against cryptanalysis and the quality of the cipher text obtained, this system is subjected to different tests, such as entropy, correlation coefficient, χ2, Changing Pixel Rate (NPCR), and Unified Averaged Changing Intensity (UACI). The results are shown and compared with recently proposed systems.

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles, then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

In the source camera linking (SCL) tasks, a large number of images taken by the same camera is not available, then forensic investigation must be carried out using only residual noises extracted from each natural image without any knowledge about their source camera. Therefore, an efficient denoising algorithm and/or noise enhancement function are required to estimate accurately Sensor Pattern Noise (SPN). In this paper we provide a systematic evaluation of common-used denoising algorithms with different parameters under a SCL task. The denoising algorithms considered are locally adaptive window-based denoising and the Block-matching 3D (BM3D) denoising. The SCL task used for evaluation is image clustering based on their source camera, in which we construct three sets with natural images taken by 5, 10 and 15 different source cameras. Linkage clustering algorithm with Ward modality is applied to group the images by their source camera. The experimental results show that the BM3D denoising with standard \((\sigma )\) deviation of \(5\) provides the best performance. Said method achieved a clustering accuracy of 98%, 96% and 85% for 5, 10 and 15 cameras respectively.

COVID-2019 is a global threat, for this reason around the world, researches have been focused on topics such as to detect it, prevent it, cure it, and predict it. Different analyses propose models to predict the evolution of this epidemic. These analyses propose models for specific geographical areas, specific countries, or create a global model. The models give us the possibility to predict the virus behavior, it could be used to make future response plans. This work presents an analysis of COVID-19 spread that shows a different angle for the whole world, through 6 geographic regions (continents). We propose to create a relationship between the countries, which are in the same geographical area to predict the advance of the virus. The countries in the same geographic region have variables with similar values (quantifiable and non-quantifiable), which affect the spread of the virus. We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others.

This paper shows the feasibility of a variant of the Generative Adversarial Network (GAN), called Star GAN, for music genre transfer. This method is noteworthy in that it simultaneously learns many-to-many mappings across different attribute domains using a single generator network. A similar architecture to research in MuseGAN and CycleGAN is applied. Also, as in MGTGAN, Desert Camel MIDI dataset is use for training and testing.

The intention of this article is to implement a system of detection and segmentation of human silhouettes, the above mentioned tasks present a great challenge in security topics and innovation, in the last years and mainly on automated video surveillance systems, which require understanding the presence and human interaction in video sequences, e.g. Human Computer Interaction (HCI), Human Behaviour comprehension, Human fall detection, among others, but the most important is behavioural biometrics, this paper tackles the common step in these research areas: the Human silhouette extraction through the bounding box. To evaluate the proposed system, standardized databases where used and also proper videos are obtained trying to emulate real-world scenarios, where the quality and the distance are factors that have demonstrated challenges for the detection with computer vision and machine learning.

Facial recognition systems has captivated research attention in recent years. Facial recognition technology is often required in real-time systems. With the rapid development, diverse algorithms of machine learning for detection and facial recognition have been proposed to address the challenges existing. In the present paper we proposed a system for facial detection and recognition under unconstrained conditions in video sequences. We analyze learning based and hand-crafted feature extraction approaches that have demonstrated high performance in task of facial recognition. In the proposed system, we compare different traditional algorithms with the avant-garde algorithms of facial recognition based on approaches discussed. The experiments on unconstrained datasets to study the face detection and face recognition show that learning based algorithms achieves a remarkable performance to face the challenges in real-time systems.

In this paper, a classification of mosquito’s specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60,000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

This work addresses the use of the MOTIF algorithm for face feature extraction. The MOTIF algorithm is commonly used to characterize texture and shows good performance in this task; a MOTIF algorithm without the Co-occurrence Matrix is proposed to obtain face features, and the approach proves to be effective. System testing was based on a standard database (the AR Face database) that includes 120 people, 70 images with face expressions and 30 with sunglasses; 1 to 9 images were used to make the template for each person. After using Euclidean distance, Cosine distance and support vector machine as classifiers, correct classification was achieved with 98% accuracy. Further tests were performed with all databases and compared with Local Binary Pattern, DI-WBP and other commonly used schemes, demonstrating effective face recognition by the MOTIF algorithm without the co-occurrence matrix in addition to its fast performance due to the low computational cost.

In recent years, end users can easily capture digital images using several devices, such as smartphones, mobile devices and digital imaging cameras, allowing such images to be easily copied, manipulated, transmitted or format converted without any restrictions. This fact suggests the necessity to develop digital tools, such as digital watermarking, to solve the issues associated with copyright protection and ownership authentication of digital images. To claim the ownership of a digital image, we propose a camouflaged, unseen-visible watermarking technique based on luminance and texture properties in conjunction with an image enhancement criterion. The proposed method has some advantages over invisible and visible watermarking methodologies in terms of readability and imperceptibility of the watermark, respectively. The experimental results demonstrate that the proposed scheme is effective and applicable for digital images on a variety of topics, including natural scenes and man-made objects, both indoors and outdoors. A comparison with previously reported methods based on unseen-visible watermarking techniques is also provided.

An imperceptible visible watermarking (IVW) algorithm is proposed to deliver auxiliary information about the visual contents, including ownership information. The proposed algorithm overcomes several inconveniences presented in previously reported IVW algorithms using the just noticeable difference (JND) criterion, invisible watermarking and binarisation function. The proposed algorithm consists of the visible watermark embedding and the exhibition stages. The JND criterion is used to embed visible watermark pattern in an imperceptible manner by the human visual system, while invisible watermarking based on the discrete cosine transform is used to share crucial parameters between both stages, which makes possible the watermark visualisation without any side information. In the watermark exhibition stage, the imperceptibly embedded visible watermark pattern is exhibited by the binarisation function. The proposed algorithm can be applied to any class of images with different characteristics and several visible watermark patterns can be embedded into an input image. Evaluation results show that performance of the proposed algorithm is better compared with previously reported algorithms from several practical points of view.