Comparative performance assessment of deep learning based image steganography techniques (original) (raw)
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Hiding Data in Images Using Cryptography and Deep Neural Network
Journal of Artificial Intelligence and Systems, 2019
Steganography is an art of obscuring data inside another quotidian file of similar or varying types. Hiding data has always been of significant importance to digital forensics. Previously, steganography has been combined with cryptography and neural networks separately. Whereas, this research combines steganography, cryptography with the neural networks all together to hide an image inside another container image of the larger or same size. Although the cryptographic technique used is quite simple, but is effective when convoluted with deep neural nets. Other steganography techniques involve hiding data efficiently, but in a uniform pattern which makes it less secure. This method targets both the challenges and make data hiding secure and non-uniform.
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.
Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over Cloud
SN Computer Science , 2024
Image steganography plays a pivotal role in secure data communication and confidentiality protection, particularly in cloud-based environments. In this study, we propose a novel hybrid approach, CNN-DCT Steganography, which combines the power of convolutional neural networks (CNNs) and discrete cosine transform (DCT) for efficient and secure data hiding within images over cloud storage. The proposed method capitalizes on the robust feature extraction capabilities of CNNs and the spatial frequency domain transformation of DCT to achieve imperceptible embedding and enhanced data-hiding capacity. In the proposed CNN-DCT Steganography approach, the cover image undergoes a two-step process. First, feature extraction using a deep CNN enables the selection of appropriate regions for data embedding, ensuring minimal visual distortions. Next, the selected regions are subjected to the DCT-based steganography technique, where secret data is seamlessly embedded into the image, rendering it visually indistinguishable from the original. To evaluate the effectiveness of our approach, extensive experiments are conducted using a diverse dataset comprising 500 high-resolution images. Comparative analysis with existing steganography methods demonstrates the superiority of the proposed CNN-DCT Steganography approach. The results showcase higher data hiding capacity, superior visual quality with an MSE of 112.5, steganalysis resistance with a false positive rate of 2.1%, and accurate data retrieval with a bit error rate of 0.028. Furthermore, the proposed method exhibits robustness against common image transformations, ensuring the integrity of the concealed data even under various modifications. Moreover, the computational efficiency of our approach is demonstrated by a competitive execution time of 2.3 s, making it feasible for real-world cloud-based applications. The combination of deep learning techniques and DCT-based steganography ensures a balance between security and visual quality, making our approach ideal for scenarios where data confidentiality and authenticity are paramount. In conclusion, the CNN-DCT Steganography approach represents a significant advancement in image steganography over cloud storage. Its capability to efficiently hide data, maintain visual fidelity, resist steganalysis, and withstand image transformations positions it as a promising solution for secure image communication and sharing. By continuously refining and extending this hybrid model, we pave the way for enhanced data protection and secure cloud-based information exchange in the digital era.
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.
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.
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 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...
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.
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.