Application Of Toral Automorphisms to Preserve Confidentiality Principle in Video Live Streaming

Most of the Live Video Systems do not preserve the Confidentiality principle, and send all frames of the video without any protection, allowing an easy “man in the middle” attack. But when it does, it uses cryptographic techniques over streaming data or makes use of secure channel systems. This generates low frame rate and demands many processor resources. In fact native Live Video Streaming demands many resources of all System. In this paper we propose a technique to preserve confidentiality in Video Live Streaming applying a confusing visual method making use of the Toral Automorphism Spatial Transformation over each frame. In terms of agreeing robustness to this algorithm, we agree on two criteria: (1) Before reallocating subframes, rotate some of them 180°; and (2) Randomly choose a key to change the order of reallocating subframes. Keywords: toral automorphism, spatial transformation, subframe, man in the middle, iterations

System for creating and authentication credentials

System for creating and authentication credentialsThis article present a system for creating and credential authentication personal identi- fication (ID) safe, using one-dimensional and two-dimensional barcodes, data encryption and symmetric key technique watermark. To do this, a watermark is inserted on the photo of the user, generated from a unique identification code which will be available in printed credential so that at the time of validation of the credential is carried by calculating the cross-correlation between the watermark contained in the photograph of the user and the watermark calculated at the time of validation using the same unique identification code. We show that proper selection of parameters for inserting the watermark: length, gain, position of the watermark and decision threshold, are essential to ensure the proper functioning of the proposed scheme, ensuring maintain sufficient quality in visual image to the user recognition and in turn be robust enough to withstand the attack of converting digital-analog (D / A) and analog-digital (A/D)

Face Recognition Based on Texture Descriptors

In this chapter, the performance of different texture descriptor algorithms used in face feature extraction tasks are analyzed. These commonly used algorithms to extract texture characteristics from images, with quite good results in this task, are also expected to provide fairly good results when used to characterize the face in an image. To perform the testing task, an AR face database, which is a standard database that contains images of 120 people, was used, including 70 images with different facial expressions and 30 with sunglasses, and all of them with different illumination intensity. To train the recognition system from one to seven images were used for each person. Different classifiers like Euclidean distance, cosine distance, and support vector machine (SVM) were also used, and the results obtained were higher than 98% for classification, achieving a good performance in verification task. This chapter was also compared with other schemes, showing the effectiveness of all of them

UNA COMPARACIÓN DE REDUCCIÓN DE RUIDO EN IMÁGENES DIGITALES UTILIZANDO UN MODELADO ESTADÍSTICO DE COEFICIENTES WAVELET Y FILTRADO DE WIENER

Este trabajo presenta un método de disminución de ruido en imágenes digitales, basado en un enfoque Bayesiano de dos etapas con ajuste empírico. Se estiman los coeficientes de una transformada wavelet de la imagen donde se ha reducido el ruido, utilizando una estimación lineal con un criterio de minimización del error cuadrático medio. Estos coeficientes constituyen una estimación deseable de la varianza de los coeficientes wavelet de la imagen libre de ruido

Empirical Study of the Associative Approach in the Context of Classification Problems

Research carried out by the scientific community has shown that the performance of the classifiers depends not only on the learning rule, if not also on the complexities inherent in the data sets. Some traditional classifiers have been commonly used in the context of classification problems (three Neural Networks, C4.5, SVM, among others). However, the associative approach has been further explored in the recovery context, than in the classification task, and its performance almost has not been analyzed when several complexities in the data are presented. The present investigation analyzes the performance of the associative approach (CHA, CHAT and original Alpha Beta) when three classification problems occur (class imbalance, overlapping and a typical patterns). The results show that the CHAT algorithm recognizes the minority class better than the rest of the classifiers in the context of class imbalance. However, the CHA model ignores the minority class in most cases. In addition, the CHAT algorithm requires well-defined decision boundaries when Wilson’s method is applied, because of its performance increases. Also, it was noted that when a balance between the rates is emphasized, the performance of the three classifiers increase (RB, RFBR and CHAT). The original Alfa Beta model shows poor performance when pre-processing the data is done. The performance of the classifiers increases significantly when the SMOTE method is applied, which does not occur without a pre-processing or with a subsampling, in the context of the imbalance of the classes.Investigaciones realizadas por la comunidad científica han evidenciado que el rendimiento de los clasificadores, no solamente depende de la regla de aprendizaje, sino también de las complejidades inherentes en los conjuntos de datos. Algunos clasificadores se han utilizado habitualmente en el contexto de losproblemas de clasificación (tres Redes neuronales, C4.5, SVM, entre otros). No obstante, el enfoque asociativo se ha explorado más en en el ámbito de recuperación, que en la tarea de clasificación, y su rendimiento se ha analizado escasamente cuando se presentan varias complejidades en los datos. La presente investigación analiza el rendimiento del enfoque asociativo (CHA, CHAT y Alfa Beta original) cuando se presentan tres problemas de clasificación (desequilibrio de las clases, solapamiento y patrones atípicos). Los resultados evidencian que el CHAT reconoce mejor la clase minoritaria en comparación con el resto de los clasificadores en el contexto del desequilibrio de las clases. Sin embargo, el modelo CHA ignora la clase minoritaria en la mayoría de los casos. Además, el modelo CHAT exhibe la necesidad de requerir de fronteras de decisión bien definidas cuando se aplica el método de Wilson, ya que su rendimiento se incrementa. También, se notó que cuando se enfatiza un equilibrio entre las tasas, el rendimiento de tres clasificadores incrementa (CHAT, RB y RFBR). El modelo Alfa beta original sigue mostrando un desempeño pobre cuando se realiza el pre-procesamiento en los datos. El rendimiento de los clasificadores incrementa significativamente al aplicarse el método SMOTE, situación que no se presenta sin un pre-procesamiento o submuestreo, en el contexto del desequilibrio de las clases