@mastersthesis {xDomenech, title = {Hiding Images in their Spoken Narratives}, year = {2022}, abstract = {

Steganography is the technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid its detection. Throughout our work, we study the case where the hidden secret data is an image and the non-secret data or cover signal is an audio. To this end, we use a recently proposed residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. In our work, we evaluate the above mentioned residual steganography architecture with the Localized Narratives dataset, explore the feasibility of using short-time fourier transform (STFT) audio spectrograms instead of STDCTs to improve the efficiency of the system, investigate the use of hidden signals permuted with the objective to spread the audio corruption of the revealed images, apply averaged audio windows to improve quality results and tested the system in real-world distortions.

}, author = {Teresa Domenech}, editor = {McGuinness, Kevin and Pons, Jordi and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xDomenech19, title = {Clasificaci{\'o}n de im{\'a}genes dermatosc{\'o}picas utilizando Redes Neuronales Convolucionales e informaci{\'o}n de metadatos}, year = {2019}, abstract = {

Convolutional Neural Networks (CNNs) is a technology that is evolving very quickly. The CNNs are used in image recognition to improve computer vision systems in image classification, object detection and segmentation tasks. For all the mentioned features we can apply the CNN in the field of medicine to automate the techniques of obtaining diagnosis and facilitate the tasks of doctors.

The purpose of this project is to classify images of different skin lesions in order to detect possible diseases such as skin cancer (melanoma) through CNNs and metadata information. The images used in this project have been provided by the Dermatology Department of the Clinical Hospital of Barcelona

}, author = {Teresa Domenech}, editor = {Ver{\'o}nica Vilaplana} }