136 research outputs found
Learning Iterative Neural Optimizers for Image Steganography
Image steganography is the process of concealing secret information in images
through imperceptible changes. Recent work has formulated this task as a
classic constrained optimization problem. In this paper, we argue that image
steganography is inherently performed on the (elusive) manifold of natural
images, and propose an iterative neural network trained to perform the
optimization steps. In contrast to classical optimization methods like L-BFGS
or projected gradient descent, we train the neural network to also stay close
to the manifold of natural images throughout the optimization. We show that our
learned neural optimization is faster and more reliable than classical
optimization approaches. In comparison to previous state-of-the-art
encoder-decoder-based steganography methods, it reduces the recovery error rate
by multiple orders of magnitude and achieves zero error up to 3 bits per pixel
(bpp) without the need for error-correcting codes.Comment: International Conference on Learning Representations (ICLR) 202
Convolutional Neural Networks for Image Steganalysis in the Spatial Domain
Esta tesis doctoral muestra los resultados obtenidos al aplicar Redes Neuronales Convolucionales (CNNs) para el estegoanálisis de imágenes digitales en el dominio espacial. La esteganografía consiste en ocultar mensajes dentro de un objeto conocido como portador para establecer un canal de comunicación encubierto para que el acto de comunicación pase desapercibido para los observadores que tienen acceso a ese canal. Steganalysis se dedica a detectar mensajes ocultos mediante esteganografía; estos mensajes pueden estar implícitos en diferentes tipos de medios, como imágenes digitales, archivos de video, archivos de audio o texto sin formato. Desde 2014, los investigadores se han interesado especialmente en aplicar técnicas de Deep Learning (DL) para lograr resultados que superen los métodos tradicionales de Machine Learning (ML).Is doctoral thesis shows the results obtained by applying Convolutional Neural Networks (CNNs) for the steganalysis of digital images in the spatial domain. Steganography consists of hiding messages inside an object known as a carrier to establish a covert communication channel so that the act of communication goes unnoticed by observers who have access to that channel. Steganalysis is dedicated to detecting hidden messages using steganography; these messages can be implicit in di.erent types of media, such as digital images, video €les, audio €les, or plain text. Since 2014 researchers have taken a particular interest in applying Deep Learning (DL) techniques to achieving results that surpass traditional Machine Learning (ML) methods
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