@conference {cGeleta21, title = {PixInWav: Residual Steganography for Hiding Pixels in Audio}, booktitle = {ICASSP}, year = {2022}, month = {06/2021}, abstract = {

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline --- which can be later hidden, in a residual fashion, into any audio signal. Finally, we test our scheme in a lab setting to transmit images over airwaves from a loudspeaker to a microphone verifying our theoretical insights and obtaining promising results.

\ Deep Learning Barcelona Symposium 2022

Presentation from the early stages of the project (January 2021):

}, author = {Geleta, Margarita and Punt{\'\i}, Cristina and McGuinness, Kevin and Pons, Jordi and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xPunti20, title = {PixInPix: Hidding Pixels in Pixels}, year = {2020}, abstract = {

PixInPix is an steganography hidding system of images within other images.\ The system designed is able to create, from an cover image and a message, a new steganography image. This new stego-image looks as similar as possible as the cover but has the message hidden in it.\ Our approach adopts the U-net architecture and combines two reconstruction losses to provide a simple yet effective approach tested in low resolution images from MNIST, CIFAR and ImageNet.

}, author = {Punt{\'\i}, Cristina}, editor = {McGuinness, Kevin and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cAssensa, title = {PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC)}, year = {2018}, month = {07/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the Salient360! dataset.

This work obtained the\ 2nd award in Prediction of Head-gaze Scan-paths for Images, and the 2nd award in Prediction of Eye-gaze Scan-paths for Images at the IEEE ICME 2018 Salient360! Challenge.

}, doi = {10.1007/978-3-030-11021-5_25}, url = {https://doi.org/10.1007/978-3-030-11021-5_25}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} }