Showing papers on "Steganography published in 2021"

PSNR vs SSIM: imperceptibility quality assessment for image steganography

Abstract: Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.

Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features

Abstract: With the coming era of cloud technology, cloud storage is an emerging technology to store massive digital images, which provides steganography a new fashion to embed secret information into massive images Specifically, a resourceful steganographer could embed a set of secret information into multiple images adaptively, and share these images in cloud storage with the receiver, instead of traditional single image steganography Nevertheless, it is still an open issue how to allocate embedding payload among a sequence of images for security performance enhancement This paper formulates adaptive payload distribution in multiple images steganography based on image texture features and provides the theoretical security analysis from the steganalyst's point of view Two payload distribution strategies based on image texture complexity and distortion distribution are designed and discussed respectively The proposed strategies can be employed together with these state-of-the-art single image steganographic algorithms The comparisons of the security performance against the modern universal pooled steganalysis are given Furthermore, this paper compares the per image detectability of these multiple images steganographic schemes against the modern single image steganalyzer Extensive experimental results show that the proposed payload distribution strategies could obtain better security performance

VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder

Abstract: In recent years, linguistic steganography based on text auto-generation technology has been greatly developed, which is considered to be a very promising but also a very challenging research topic. Previous works mainly focus on optimizing the language model and conditional probability coding methods, aiming at generating steganographic sentences with better quality. In this paper, we first report some of our latest experimental findings, which seem to indicate that the quality of the generated steganographic text cannot fully guarantee its steganographic security, and even has a prominent perceptual-imperceptibility and statistical-imperceptibility conflict effect (Psic Effect). To further improve the imperceptibility and security of generated steganographic texts, in this paper, we propose a new linguistic steganography based on Variational Auto-Encoder (VAE), which can be called VAE-Stega. We use the encoder in VAE-Stega to learn the overall statistical distribution characteristics of a large number of normal texts, and then use the decoder in VAE-Stega to generate steganographic sentences which conform to both of the statistical language model as well as the overall statistical distribution of normal sentences, so as to guarantee both the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic texts at the same time. We design several experiments to test the proposed method. Experimental results show that the proposed model can greatly improve the imperceptibility of the generated steganographic sentences and thus achieves the state of the art performance.

A Siamese CNN for Image Steganalysis

Abstract: Image steganalysis is a technique for detecting data hidden in images. Recent research has shown the powerful capabilities of using convolutional neural networks (CNN) for image steganalysis. However, due to the particularity of steganographic signals, there are still few reliable CNN-based methods for applying steganalysis to images of arbitrary size. In this paper, we address this issue by exploring the possibility of exploiting a network for steganalyzing images of varying sizes without retraining its parameters. On the assumption that natural image noise is similar between different image sub-regions, we propose an end-to-end, deep learning, novel solution for distinguishing steganography images from normal images that provides satisfying performance. The proposed network first takes the image as the input, then identifies the relationships between the noise of different image sub-regions, and, finally, outputs the resulting classification based upon them. Our algorithm adopts a Siamese, CNN-based architecture, which consists of two symmetrical subnets with shared parameters, and contains three phases: preprocessing, feature extraction, and fusion/classification. To validate the network, we generated datasets composed of steganography images with multiple sizes and their corresponding normal images sourced from BOSSbase 1.01 and ALASKA #2. Experimental results produced by the data generated by various methods show that our proposed network is well-generalized and robust.

An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning

Abstract: Automatic cost learning for steganography based on deep neural networks is receiving increasing attention. Steganographic methods under such a framework have been shown to achieve better security performance than methods adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality, including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.

Large-capacity Image Steganography Based on Invertible Neural Networks

Abstract: Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

Invisible Backdoor Attacks on Deep Neural Networks Via Steganography and Regularization

Abstract: Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this article, we create covert and scattered triggers for backdoor attacks, invisible backdoors , where triggers can fool both DNN models and human inspection. We apply our invisible backdoors through two state-of-the-art methods of embedding triggers for backdoor attacks. The first approach on Badnets embeds the trigger into DNNs through steganography. The second approach of a trojan attack uses two types of additional regularization terms to generate the triggers with irregular shape and size. We use the Attack Success Rate and Functionality to measure the performance of our attacks. We introduce two novel definitions of invisibility for human perception; one is conceptualized by the Perceptual Adversarial Similarity Score (PASS) and the other is Learned Perceptual Image Patch Similarity (LPIPS). We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as four datasets MNIST, CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates for the adversary, functionality for the normal users, and invisibility scores for the administrators. We finally argue that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches.

FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis

Abstract: The protection of user private data has long been the focus of AI security. We know that training machine learning models rely on large amounts of user data. However, user data often exists in the form of isolated islands that can not be integrated under many secure and legal constraints. The large-scale application of image steganalysis algorithms in real life is still not satisfactory due to the following challenges. First, it is difficult to aggregate all of the scattered steganographic images to train a robust classifier. Second, even if the images are encrypted, participants do not want irrelevant people to peek into the hidden information, resulting in the disclosure of private data. Finally, it is often impossible for different participants to train their tailored models. In this paper, we introduce a novel framework, referred to as FedSteg, to train a secure, personalized distributed model through federated transfer learning to fulfill secure image steganalysis. Extensive experiments on detecting several state-of-the-art steganographic methods i.e., WOW, S-UNIWARD, and HILL, validate that FedSteg achieves certain improvements compared to traditional non-federated steganalysis approaches. In addition, FedSteg is highly extensible and can be easily employed to various large-scale secure steganographic recognition tasks.

Enhanced Data Security of Communication System Using Combined Encryption and Steganography

Abstract: Data security has become a paramount necessity and more obligation in daily life. Most of our systems can be hacked, and it causes very high risks to our confidential files inside the systems. Therefore, for various security reasons, we use various methods to save as much as possible on this data, regardless of its different forms, texts, pictures, videos, etc. In this paper, we mainly rely on storing the basic image which should be protected in another image after changing its formal to composites using the DWT wavelet transform. The process of zeroing sites and storing their contents technique is used to carry the components of the main image. Then process them mathematically by using the exponential function. The result of this process is to obtain a fully encrypted image. The image required to be protected from detection and discrimination is hidden behind the encrypted image. The proposed system contains two algorithms. the first algorithm is used for encryption and hiding, but the second algorithm is designed for returning and decoding the main image to its original state with very efficiently.

Hiding Data Using Efficient Combination of RSA Cryptography, and Compression Steganography Techniques

Abstract: Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest–Shamir–Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image’s dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Abstract: Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.

Steganography and Steganalysis (in digital forensics): a Cybersecurity guide

Abstract: Steganography and steganalysis is a relatively new-fangled scientific discipline in security systems and digital forensics, respectively, but one that has matured greatly over the past two decades. In any specialism of human endeavour, it is imperative to periodically pause and review the state of the discipline for what has been achieved till date. This article scrutinizes where the discipline of steganography and steganalysis at this point in time in context to the common user and new researchers in terms of current trends. Also, what has been accomplished in order to critically examine what has been done well and what ought to be done better. The state-of-the-art techniques for steganography and steganalysis (image and video) have been deliberated for the last 5 years literature. Further, the paper also takes stock the dataset and tools available for multimedia steganography and steganalysis with the examples where steganography has been used in real-life. It is a corpus of the author’s opinion and the viewpoints of different other researchers and practitioners, working in this discipline. Additionally, experiments were done using image steganography techniques to analyse the recent trends. This survey is intended to provide a complete guide for common people and new researchers and scholars approaching this field, sight on the existing and the future of steganography and steganalysis.

Secure Robust JPEG Steganography Based on AutoEncoder With Adaptive BCH Encoding

Abstract: Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security.

Analysis of various data security techniques of steganography: A survey

Abstract: The amount of data exchanged via the Internet is increasing nowadays. Hence, data security is termed as a serious issue while communication of data is processed over the Internet. Everyone needs th...

Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model

Abstract: Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependencies among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers.

Coverless Image Steganography Based on Multi-Object Recognition

Abstract: Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multi-objects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker’s suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, $3.1\times $ robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.

An efficient quantum image steganography protocol based on improved EMD algorithm

Abstract: This paper proposes an efficiency quantum image steganography protocol based on improved exploiting modification direction algorithm (QIS-IEMD). The new methods of expanding modification range and dynamically sharing between subgroups are introduced in the new protocol. Compared with the quantum image steganography protocol based on EMD algorithm (QIS-EMD), secret information can be embedded on multiple bit planes after expanding pixel’s modification range, and the carrier pixel groups can be dynamically shared to achieve higher embedding efficiency, embedding rate and larger capacity. In addition, in order to reflect the feasibility and practicability of QIS-IEMD, dedicated quantum circuits for embedding and extracting secret information are designed for it. Finally, the experimental results and detailed mathematical analysis prove that, compared with QIS-EMD, QIS-IEMD not only performs well in imperceptibility, but also significantly increases embedding efficiency, embedding rate and capacity.

An adjustable visual image cryptosystem based on 6D hyperchaotic system and compressive sensing

Abstract: In this paper, an efficient and adjustable visual image encryption scheme is proposed by combining a 6D hyperchaotic system, compressive sensing, and Bezier curve embedding. First, the plain image is sparse by discrete wavelet transform (DWT). Then, the sparse image is encrypted and compressed through game-of-life (GOL) hybrid scrambling and compressive sensing into a cipher image. Next, Bezier curve embedding is utilized to embed the cipher image into the carrier image in wavelet domain. After these operations, the final visually meaningful steganographic image is generated. Additionally, the frequency-domain information of the plain image is used to generate the initial values of the 6D hyperchaotic system in scrambling process, which makes the proposed encryption scheme able to effectively resist the chosen-plaintext attacks (CPA) and the known-plaintext attacks (KPA). Moreover, our scheme exhibits excellent adjustable performance compared with existing related schemes. Ultimately, simulation results and comprehensive performance analyses demonstrate that the scheme proposed in this paper has high decryption quality, visual security, robustness, and operating efficiency.

Encryption System for Hiding Information Based on Internet of Things

Abstract: One of the unexpected intelligence tactics known in World War II was to conceal the data in images that were reduced to the size of a point that was used in every text and transported in front of the enemy's eyes. In the new age, and after the expansion of Internet science and the use of the Internet worldwide, we will establish a security feature of the IOT service that will work more reliably and more effectively to deal with the Internet of Things and ensure the work of the services that the customer interacts with. A secret-key stenographic scheme that embeds four gray-scale secret size (128*128) pixel images into a size (512*512) pixel cover image in this work. Wavelet transform is the method used in this project to analyze the cover into its frequency components. In this work, combinations of steganography and cryptography were made to increase the level of safety and make the device more difficult for attackers to beat. The resulting stego-image that will be transmitted did not raise any suspicion by both objective and subjective evaluation, so the primary objective of Steganography is achieved. The proposed system was designed by using (MATLAB R2018b) and running on a Pentium-4 computer. The Internet of Things works with the encryption system for data in a synchronized manner with the technological development, and in order to maintain the stability of any Internet of things service, whether it is information signal services, visual or audio data, a remote control system, or data storage in the Internet cloud, we must focus on data preservation from internet pirates and internet system hackers. The picture Figure 4 below shows the method of encryption and dealing with the Internet of things system..

An enhanced Huffman-PSO based image optimization algorithm for image steganography

Abstract: It is crucial in the field of image steganography to find an algorithm for hiding information by using various combinations of compression techniques. The primary factors in this research are maximizing the capacity and improving the quality of the image. The image quality cannot be compromised up to a certain level as it breaks the concept of steganography by getting distorted visibly. The second primary factor is maximizing the data-carrying/embedding capacity, which makes the use of this technique more efficient. In this paper, we are proposing an image steganography tool by using Huffman Encoding and Particle Swarm Optimization, which will improve the performance of the information hiding scheme and improve overall efficiency. The combinational technique of Huffman PSO not only offers higher information embedment capabilities but also maintains the image quality. The experimental analysis and results on cover images along with different sizes of secret messages validate that the proposed HPSO scheme has superior results using parameters Peak-Signal-to-Noise-Ratio, Mean Square Error, Bit Error Rate, and Structural Similarity Index. It is also robust against statistical attacks.

Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

Abstract: The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient’s information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an Adaptive Genetic Algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The proposed model can also prevent attacks, such as steganalysis or RS attacks.

Image steganography using deep learning based edge detection

Abstract: This paper introduces a deep learning-based Steganography method for hiding secret information within the cover image. For this, we use a convolutional neural network (CNN) with Deep Supervision based edge detector, which can retain more edge pixels over conventional edge detection algorithms. Initially, the cover image is pre-processed by masking the last 5-bits of each pixel. The said edge detector model is then applied to obtain a gray-scale edge map. To get the prominent edge information, the gray-scale edge map is converted into a binary version using both global and adaptive binarization schemes. The purpose of using different binarization techniques is to prove the less sensitive nature of the edge detection method to the thresholding approaches. Our rule for embedding secret bits within the cover image is as follows: more bits into the edge pixels while fewer bits into the non-edge pixels. Experimental outcomes on various standard images confirm that compared to state-of-the-art methods, the proposed method achieves a higher payload.

Efficient HEVC steganography approach based on audio compression and encryption in QFFT domain for secure multimedia communication

Abstract: High-Efficiency Video Coding (HEVC) is the most recent video codec standard. It is substantial to analyze the HEVC steganography process due to its practical and academic significance. Thus, a secure HEVC steganography approach is introduced in this paper to study the possibility of hiding an encrypted secret audio message within a cover compressed video frame in a secure and complicated manner. In the preliminary stage, the secret audio message is compressed utilizing the Discrete Cosine Transform (DCT) to achieve a high capacity performance for the HEVC steganography process. After that, the suggested approach implies two-cascaded encryption layers to encrypt the compressed secret message before embedding it within a cover HEVC frame. In the first encryption layer, a novel encryption technique based on random projection and Legendre sequence in the Discrete Wavelet Transform (DWT) domain is introduced to cipher the compressed secret audio message. In the second encryption layer, the yielded encrypted audio message is represented in a form of quaternion numbers using the Quaternion Fast Fourier Transform (QFFT) technique. Each cover HEVC frame is also represented in a quaternion form. In the suggested approach, some straightforward quaternion mathematical operations are employed on the encrypted secret message and the cover HEVC frames to represent them in a quaternion form in the frequency domain, then the encrypted secret audio message is hidden within the cover HEVC frame. At the receiver, the secret message can be retrieved and extracted from the cover HEVC frame utilizing the same methodology of the employed quaternion mathematical operations. The major contributions of the suggested HEVC steganography scheme are: (1) it allows hiding of massive amount of secret information within cover video frames, and (2) it has higher robustness against multimedia attacks and steganalysis contrasted to the conventional and literature schemes. Furthermore, the proposed approach is evaluated utilizing different assessment metrics like Feature Similarity Index Measure (FSIM), Peak Signal-to-Noise Ratio (PSNR), correlation coefficient, and Structural Similarity Index Measure (SSIM) to evaluate the efficiency of the stego HEVC frames compared to the original ones. The achieved outcomes demonstrate that the suggested steganography scheme is straightforward to implement, more secure, and robust in the presence of steganalysis multimedia attacks compared to the literature approaches.

Improving EfficientNet for JPEG Steganalysis

Abstract: In this paper, we study the EfficientNet family pre-trained on ImageNet when used for steganalysis using transfer learning. We show that certain "surgical modifications" aimed at maintaining the input resolution in EfficientNet architectures significantly boost their performance in JPEG steganalysis, establishing thus new benchmarks. The modified models are evaluated by their detection accuracy, the number of parameters, the memory consumption, and the total floating point operations (FLOPs) on the ALASKA II dataset. We also show that, surprisingly, EfficientNets in their "vanilla form" do not perform as well as the SRNet in BOSSbase+BOWS2. This is because, unlike ALASKA II images, BOSSbase+BOWS2 contains aggressively subsampled images with more complex content. The surgical modifications in EfficientNet remedy this underperformance as well.

PSO-Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture.

Abstract: Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 [COVID-19]) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors' knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganography-based blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

Digital Image Security Based on the Hybrid Model of Image Hiding and Encryption

Abstract: In today's world's internet is a major communication method to communicate from one end to other, and image is mostly used digital content which are distributed on the internet but securely data communication through the internet is major issue today. To secure data or image many techniques are used in which hiding and encryption is the most popular techniques for image or data security. In this paper proposed a hybrid model of digital image security technique based on the image hiding and encryption. In this proposed technique, 2-level of security is to be provided to a secret image. The secret image is first hidden within a cover image using LSB hiding technique and after that got the stego image. Now after getting the stego image, the stego image is encrypted using AES encryption algorithm.