Hiding Data in Images Using Cryptography and Deep Neural Network (original) (raw)
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IET Image Processing, 2021
Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
An image is the most popular media format amongst the current modern digital generation. Encoding binary data within an image is an easy way to hide the secret image. Broadly speaking, steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. Steganography helps us as the intended secret message does not attract attention to itself as an object of scrutiny. Plainly visible encrypted messages might be better protected but they arouse interest and may in themselves be incriminating in countries in which encryption is illegal. In other words, steganography is more discreet than cryptography when we want to send secret information while also being easier to extract. The usual implementations tend to significantly lose the image quality and are also easily detectable. However, this implementation makes efforts to overcome the existing problems of image steganography with the help of a deep neural network which results in the generation of a final image that is almost identical to the original image and isn't detectable easily.
Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography
IEEE Transactions on Computational Social Systems
Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques.
Robust steganographic method based on unconventional approach of neural networks
Applied Soft Computing, 2018
The article deals with the issue of using an apparatus of neural networks in the area of steganography. A new method called STEGONN was proposed. The proposed method is robust enough to an attack and the hidden message hard to be falsified. The core of our work lies in a design and implementation of a method for the use of neural networks as a native coder and decoder of a secret message (digital watermark) with an emphasis on the minimum necessary level of image data modification-covermedium. A covermedium is not perceived as a passive cover of a secret message, but we make active use of cover medium data, primarily its data markers (image markers) to insert a secret message. The advantage over other steganographic methods is the fact that the method implicitly offer the possibility to detect corrupted parts of the stegomedium and inform the user about possible manipulation with the image. The characteristics of the proposed method have been experimentally verified and compared with commercially available steganographic applications.
Comparative performance assessment of deep learning based image steganography techniques
Scientific Reports, 2022
Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.
IRJET- A Deep Neural Network Spatial Domain Steganography Method In Image Processing
IRJET, 2021
In the digital or data communication the most important entity is the data only. If the data is confidential then it becomes essential to wrap or encrypt the data in a manner that no one can access it or not even get a hint about it. One of the well known way of concealing the data is the principle of steganography. This methodology stores one type of data into other or similar type of data such that no one can access it. One of the way is the concealing the secret data inside the digital image so that data becomes invisible in the image. Some techniques store the data in the cover image and then perform some kind of encryption so that complexity of the algorithm will increase and eventually will become hard to find the hidden data. In the present research work a new steganography technique is proposed based on the deep neural network. The technique used various components to store the data so that no one can access it. The proposed technique is compared with other steganography techniques. Various objective parameters like peak signal to noise ratio and mean square error are used for performance evaluation of the proposed technique.
Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach
International Conference on Machine Learning for Networking, MLN 2019, 2019
Steganography is a science which helps to hide secret data inside multimedia supports like image, audio and video files to ensure secure communication between two parts of a channel. Steganalysis is the discipline which detects the presence of data hidden by a steganographic algorithm. There are two types of steganalysis: targeted steganalysis and universal steganalysis. In targeted steganalysis, the steganographic algorithm used to hide data is known. In the case of universal steganalysis, the detection of hidden data doesn’t depend on any specific algorithm used in the process of steganography. In this paper, we focus on universal steganalysis of images in a database with an eventual cover-source mismatch problem. It is shown that combining both unsupervised and supervised machine learning algorithms helps to improve the performance of classifiers in the case of universal steganalysis by reducing the cover-source mismatch problem. In the unsupervised step, the k-means algorithm is generally used to group similar images. When the number of features extracted from the image is very large it becomes difficult to compute the k-means algorithm properly. We propose, in that case, to use Deep Learning with Convolutional Neural Network (CNN) to group similar images at first and implement a Multilayer Perceptron (MLP) neural network to estimate the hidden message length in all the different groups of images. The first step of this approach prevents the cover-source mismatch problem. Reducing this issue boost the performance of classifiers in the second step which consists of estimating the hidden message length.
International Journal of Engineering and Advanced Technology, 2020
Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of deep learning techniques. We use a joint deep neural network concept it consist of two sub models. The first model is responsible for hiding digital audio into a digital image. The second model is responsible for returning a digital audio from the stego image. Various vast experiments are conducted with a set of 24K images and also for various sizes of images. From the experiments it can be seen propo...
A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis
KSII Transactions on Internet and Information Systems
Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.
Deep learning in steganography and steganalysis
Digital Media Steganography, 2020
For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of Rich Models (RM), 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). Between 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 in 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, that have been evaluated with a methodology specific to the discipline of steganalysis. The chapter is not intended 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 existing literature for the different scenarios of steganalysis, and finally, we will discuss steganography by deep learning.