Image forgery detection using error level analysis and deep learning

Many images are spread in the virtual world of social media. With the many editing software that allows so there is no doubt that many forgery images. By forensic the image using Error Level Analysis to find out the compression ratio between the original image and the fake image, because the original image compression and fake images are different. In addition to knowing whether the image is genuine or fake can analyze the metadata of the image, but the possibility of metadata can be changed. In this case the authors apply Deep Learning to recognize images of manipulations through the dataset of a fake image and original images via Error Level Analysis on each image and supporting parameters for error rate analysis. The result of our experiment is that we get the best accuracy of training 92.2% and 88.46% validation by going through 100 epoch

Compact and interpretable convolutional neural network architecture for electroencephalogram based motor imagery decoding

Recently, due to the popularity of deep learning, the applicability of deep Neural Networks (DNN) algorithms such as the convolutional neural networks (CNN) has been explored in decoding electroencephalogram (EEG) for Brain-Computer Interface (BCI) applications. This allows decoding of the EEG signals end-to-end, eliminating the tedious process of manually tuning each process in the decoding pipeline. However, the current DNN architectures, consisting of multiple hidden layers and numerous parameters, are not developed for EEG decoding and classification tasks, making them underperform when decoding EEG signals. Apart from this, a DNN is typically treated as a black box and interpreting what the network learns in solving the classification task is difficult, hindering from performing neurophysiological validation of the network. This thesis proposes an improved and compact CNN architecture for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a very compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in terms of cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which is often used as a benchmark in validating motor imagery (MI) classification algorithms, and a primary data that was initially collected to study the difference between motor imagery and mental rotation task associated motor imagery (MI+MR) BCI. The latter was also used in this study to test the plausibility of the proposed algorithm in highlighting the differences in cortical rhythms. In both datasets, the proposed Sinc adapted CNN algorithms show competitive decoding performance in comparisons with SOTA CNN models, where up to 87% decoding accuracy was achieved in BCI Competition IV dataset 2a and up to 91% decoding accuracy when using the primary MI+MR data. Such decoding performance was achieved with the lowest number of trainable parameters (26.5% - 34.1% reduction in the number of parameters compared to its non-Sinc counterpart). In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that focus on important cortical rhythms during task execution, thus allowing for the development of the proposed Spatial Filter Visualization algorithm. Such characteristic was crucial for the neurophysiological interpretation of the learned spatial features and was not previously established with the benchmarked SOTA methods

Rehabilitación para pacientes postinfarto cerebral utilizando sistemas BCI/FES

69 páginasAccording to the article “Stroke: A global response is needed”, at a worldwide level, brain strokes are the second cause of death and the third cause of disability on people (Johnson et al, 2016). Some of the complications that people who have suffered brain stroke can experience, listed by the Mayo Clinic, include paralysis or loss of muscle movement, difficulty speaking of swallowing, loss of memory or difficulty thinking, emotional problems, severe pain or changes in behavior and the ability of selfcare (Mayo Clinic, 2018). The methods of rehabilitation available right now are limited by the fact that they possess a short populational reach compared to the large amount of people who are affected by it. Such methods only manage to provide considerable results to those people who have suffered mild damages in their motor functions. In a study carried out by Dobkins, it was shown that only a 25% of the people who suffered from brain stroke were capable of eventually returning to an everyday life similar to the one of a healthy person (Dobkins, 2005). Currently, the market already offers devices of electrostimulation for the rehabilitation of motor functions using electromyography signals (electrical signals that result from muscle contractions) like the NESS H200. The people who have suffered from mild cerebral damages are able to activate this device due to the fact that most of them are still able to generate electrical impulses strong enough to be detected by electromyography (EMG) but not strong enough to surpass the action potential threshold needed to contract the muscle. For this reason, the necessity to develop a device that works under the same concept of electrostimulation mentioned previously but is not dependent on the residual motor functioning of the patient arises, and this way directly increasing the amount of people with more severe damages to their nervous system who can benefit from it. A brain computer interface (BCI) allows the user to control an external device by identifying specific brain signals and converting them into a series of digital commands. Such signals can be obtained by numerous ways, one of them being through electroencephalography (EEG) equipment. Once those signals are obtained, they are classified using a computational algorithm so that they can be further on expressed as electrical impulses in order to induce muscle contractions. Considering the fact that the brain signals generated when a motor movement is imagined (MI or motor imagery) are very similar to the signals generated when the actual movement is carried out, the activation of the electrostimulation device will not be affected by the residual motor capacity present on the affected patient.Acorde al artículo “Stroke: A global response is needed”, a nivel mundial, los accidentes cerebrovasculares son la segunda causa de muerte y la tercera causa de discapacidad en personas (Johnson et al, 2016). Algunas de las complicaciones que pueden experimentar las personas que han sufrido de derrame cerebral, listadas por la Clínica Mayo, incluyen parálisis o perdida de movimientos musculares, dificultad en el habla o tragar, pérdida de memoria o dificultad para pensar, problemas emocionales, dolores severos y cambios en comportamiento y la habilidad de cuidado personal (Mayo Clinic, 2018). Actualmente los métodos de rehabilitación disponibles se ven limitados por el hecho de que poseen un corto alcance para una población de personas afectadas tan grande. Dichos métodos solo logran brindar resultados considerablemente notables a aquellas personas que han sufrido daños leves en sus funciones motoras. En el estudio realizado por Dobkins se comprobó que solo un 25% de las personas que sufren de derrame cerebral son capaces de eventualmente retomar una vida diaria similar a la de una persona saludable (Dobkins, 2005). Hasta ahora, el mercado ya ofrece dispositivos de electroestimulación para la rehabilitación de funciones motoras utilizando señales de electromiografía (señales eléctricas que se dan nivel muscular) como el NESS H200. Las personas que han sufrido daños leves son capaces de accionarlos debido a que la mayoría todavía son capaces de generar impulsos eléctricos lo suficiente fuertes para ser detectados por dispositivos de electromiografía (EMG) pero sin embargo muy débiles para superar el potencial de acción necesario para contraer el musculo. Por esta razón surge la necesidad de desarrollar un dispositivo que trabaje bajo el mismo concepto de electroestimulación previamente mencionado pero que no sea dependiente del funcionamiento motor residual del paciente y así mismo se lograra aumentar el alcance a personas con daños más severos de su sistema nervioso. Una interfaz cerebro maquina (BCI por sus siglas en inglés) permite identificar señales cerebrales específicas y convertirlas en una serie de comandos digitales para controlar un dispositivo externo. Dichas señales cerebrales pueden obtenidas de numerosas maneras, una de ellas siendo a través de equipos de electroencefalografía (EEG). Una vez obtenidas las señales, estas son clasificadas mediante un algoritmo computacional para posteriormente expresarlas como impulsos eléctricos para poder inducir contracciones en el musculo. Considerando el hecho que las señales cerebrales generadas cuando se imagina un movimiento (imagen motora o MI por sus siglas en inglés) son muy similares a las que se generan cuando se lleva acabo tal movimiento, la activación del dispositivo de electroestimulación no se verá afectada por la capacidad motora residual que presente el paciente afectado.PregradoIngeniero(a) Biomédico(a