CNN Auto-Encoder Network Using Dilated Inception for Image Steganography

Abstract

Numerous studies have used convolutional neural networks (CNNs) in the field of information concealment as well as steganalysis, achieving promising results in terms of capacity and invisibility. In this study, we propose a CNN-based steganographic model to hide a color image within another color image. The proposed model consists of two sub-networks: the hiding network is used by the sender to conceal the secret image; and the reveal network is used by the recipient to extract the secret image from the stego image. The architecture of the concealment sub-network is inspired by the U-Net auto-encoder and benefits from the advantages of the dilated convolution. The reveal sub-network is inspired by the auto-encoder architecture. To ensure the integrity of the hidden secret image, the model is trained end to end: rather than training separately, the two sub-networks are trained simultaneously a pair of networks. The loss function is elaborated in such a way that it favors the quality of the stego image over the secret image as the stego image is the one that comes under steganalysis attacks. To validate the proposed model, we carried out several tests on a range of challenging publicly available image datasets such as ImageNet, Labeled Faces in the Wild (LFW), and PASCAL-VOC12. Our results show that the proposed method can dissimulate an image into another one with the same size, reaching an embedding capacity of 24 bit per pixel without generating visual or structural artefacts on the host image. In addition, the proposed model is generic, that is, it does not depend on the image's size or the database source.

Automation of Cephalometrics Using Machine Learning Methods

“…Also, time constraints and other commitments are considered. Convolutional networks (CNNs), occupying state of the art for various tasks in computer vision, have proven to be successful for a wide range of applications, including image classification [ 14 , 18 ], image segmentation, the alignment of images [ 16 ], the detection of facial points [ 4 ], the estimation of human postures [ 21 ], and the detection of lines on roads [ 11 , 12 ], among other tasks. Currently, it is in the field of medicine that a great trend has been seen in the use of CNN to automate the process of detecting and diagnosing diseases [ 6 , 17 , 7 , 5 ].…”

Image Hiding in Stochastic Geometric Moiré Gratings

“…It is clear that the complexity of the cost functions (Equations ( 20) and ( 22)) is so high that deterministic optimization algorithms should be replaced by evolutionary optimization algorithms. Since the particle swarm optimization (PSO) algorithm is widely considered to be a robust optimization algorithm with good properties of convergence [23], further construction of stochastic gratings is performed by means of PSO (though other soft computing algorithms are also used in image steganography [24]). PSO is an algorithm inspired by the social behaviors of animal groups [23,25].…”