We present a system to hide a full color image inside another of the same size with minimal quality loss to either image. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we examine how the result is achieved and apply numerous transformations to analyze if image quality in the host and hidden image can be maintained. These transformation range from simple image manipulations to sophisticated machine learning-based adversaries. Two extensions to the basic system are presented that mitigate the possibility of discovering the content of the hidden image. With these extensions, not only can the hidden information be kept secure, but the system can be used to hide even more than a single image. Applications for this technology include image authentication, digital watermarks, finding exact regions of image manipulation, and storing meta-information about image rendering and content.

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Hiding Images within Images

For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of rich models (RMs), followed by classification using an ensemble classifier (EC). In 2015 the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by deep learning approaching the performances of the two-step approach (EC + RM). During 2015–2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, selection-channel-aware steganalysis, and quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of current methods, by presenting different neural networks from the period 2015–2018, evaluated with a methodology specific to the discipline of steganalysis. We do not intend to repeat the basic concepts of machine learning or deep learning. So we will present the structure of a deep neural network in a generic way and present the networks proposed in the existing literature for different scenarios of steganalysis, and finally, we will discuss steganography by deep learning.

Deep learning in steganography and steganalysis

Steganography consists of hiding messages inside some object known as a carrier in order to establish a covert communication channel so that the act of communication itself goes unnoticed by observers who have access to that channel. The steganalysis is dedicated to the detection of hidden messages using steganography; these messages can be implicit in different types of media, such as digital images, video files, audio files or plain text. Traditionally, steganalysis has been divided into two separate stages, the first stage consists of manual extraction of sophisticated features and the second stage is classification using Ensemble Classifiers or Support Vector Machines. In recent years, the development of Deep Learning has made it possible to unify and automate the two traditional stages into an end to end approach with promising results. This paper shows the evolution of steganalysis in recent years using the Deep Learning techniques. The results of these techniques have surpassed those obtained with conventional methods - Rich Models with Ensemble Classifiers - both in the spatial and frequency (JPEG) domains. Since 2014, researchers have used The Convolutional Neural Networks to solve this problem generating diverse architectures and strategies to improve the detection percentages of steganographic images on the last generation algorithms (WOW, S-UNIWARD, HUGO, J-UNIWARD, among others). The Deep Learning, being applied to steganalysis, is now in the process of construction and results so far are encouraging for researchers that are interested in the topic.

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Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review

Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4

Advances in Deep Learning (DL) have provided alternative approaches to various complex problems, including the domain of spatial image steganalysis using Convolutional Neural Networks (CNN). Several CNN architectures have been developed in recent years, which have improved the detection accuracy of steganographic images. This work presents a novel CNN architecture which involves a preprocessing stage using filter banks to enhance steganographic noise, a feature extraction stage using depthwise and separable convolutional layers, and skip connections. Performance was evaluated using the BOSSbase 1.01 and BOWS 2 datasets with different experimental setups, including adaptive steganographic algorithms, namely WOW, S-UNIWARD, MiPOD, HILL and HUGO. Our results outperformed works published in the last few years in every experimental setting. This work improves classification accuracies on all algorithms and bits per pixel (bpp), reaching 80.3% on WOW with 0.2 bpp and 89.8% on WOW with 0.4 bpp, 73.6% and 87.1% on S-UNIWARD (0.2 and 0.4 bpp respectively), 68.3% and 81.4% on MiPOD (0.2 and 0.4 bpp), 68.5% and 81.9% on HILL (0.2 and 0.4 bpp), 74.6% and 84.5% on HUGO (0.2 and 0.4 bpp), using BOSSbase 1.01 test data.

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GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis

For steganalysis, many studies showed that convolutional neural network (CNN) has better performances than the two-part structure of traditional machine learning methods. Existing CNN architectures use various tricks to improve the performance of steganalysis, such as fixed convolutional kernels, the absolute value layer, data augmentation and the domain knowledge. However, some designing of the network structure were not extensively studied so far, such as different convolutions (inception, xception, etc.) and variety ways of pooling(spatial pyramid pooling, etc.). In this paper, we focus on designing a new CNN network structure to improve detection accuracy of spatial-domain steganography. First, we use $3\times 3$ kernels instead of the traditional $5\times 5$ kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize channel correlation of the residuals, compress the image content and increase the signal-to-noise ratio (between the stego signal and the image signal). Then, we use spatial pyramid pooling (SPP) to aggregate the local features and enhance the representation ability of features by multi-level pooling. Finally, data augmentation is adopted to further improve network performance. The experimental results show that the proposed CNN structure is significantly better than other five methods such as SRM, Ye-Net, Xu-Net, Yedroudj-Net and SRNet, when it is used to detect three spatial algorithms such as WOW, S-UNIWARD and HILL with a wide variety of datasets and payloads.

Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis

For steganalysis, many studies showed that convolutional neural network has better performances than the two-part structure of traditional machine learning methods. However, there are still two problems to be resolved: cutting down signal to noise ratio of the steganalysis feature map and steganalyzing images of arbitrary size. Some algorithms required fixed size images as the input and had low accuracy due to the underutilization of the noise residuals obtained by various types of filters. In this paper, we focus on designing an improved network structure based on CNN to resolve the above problems. First, we use 3x3 kernels instead of the traditional 5x5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize channel correlation of the residuals, compress the image content and increase the signal-to-noise ratio (between the stego signal and the image signal). Then, we use spatial pyramid pooling (SPP) to aggregate the local features, enhance the representation ability of features, and steganalyze arbitrary size image. Finally, data augmentation is adopted to further improve network performance. The experimental results show that the proposed CNN structure is significantly better than other four methods such as SRM, Ye-Net, Xu-Net, and Yedroudj-Net, when it is used to detect two spatial algorithms such as WOW and S-UNIWARAD with a wide variety of datasets and payloads.

Efficient feature learning and multi-size image steganalysis based on CNN.

In aviation communication system, VHF data-link message is used to transmit flight status, airport control instructions as well as the other sensitive information. Radio-transmitted information can be intercepted or tampered with, leading to the disclosure of private data, illegal control, and hijacking. This paper analyzed three attack routes of monitoring, entity camouflage, and man-in-the-middle attack. Man-in-the-middle attack was also able to work in next generation aviation communication networks. Two viable experiments were designed to test data-link message attacks including information leakage and the entity camouflage attack. These experiments may also be used as penetration tests of secure of aviation communication system.

Gongshen Liu

Papers

Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis

Efficient feature learning and multi-size image steganalysis based on CNN.

Analysis of Message Attacks in Aviation Data-Link Communication