Secret Image Research Articles

Extensive dual tree complex wavelet transform-based image steganography using SVD and CNN subspace

The secret data is hidden behind a cover image using digital image steganography technology that prevents the human visual system (HVS) from identifying it. The proposed image steganography method involves scrambling the secret image and then embedding it within the cover image using the dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) technique. The stego image is generated by applying the inverse of DTCWT on the processed embedded image. The PSNR values for the stego and extracted secret images are 45.51 to 30.08 dB and 45.93 dB, respectively, with an SSIM greater than 0.99 in all cases. In the final step of the proposed method, a convolutional neural network (CNN) is utilized to enhance the quality of the extracted secret image. The effectiveness of the proposed method is confirmed by the comparative results.

Optimal Deep Learning Model Enabled Secure UAV Classification for營ndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

Sharing Multiple Secrets in XOR-Based Visual Cryptography by Non-Monotonic Threshold Property

Multiple-secret visual cryptography scheme (MVCS) and fully incrementing visual cryptography scheme (FIVCS) have the same functionality that different secrets are gradually revealed by stacking different numbers of shadows. In essence, MVCS and FIVCS are the same. However, both of the two schemes suffer from large pixel expansion and deteriorated reconstructed image quality. In addition, MVCS and FIVCS require intensive computations to create base matrices. In this research, we exploit the capacity of sharing multiple secrets in XOR-based VCS (XVCS). First of all, three efficient base matrix constructions are proposed for realizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> non-monotonic XVCS (NXVCS), where the secret image is only revealed by XOR-ing exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> shadows. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> -NXVCS is adopted to constitute the multiple-secret XVCS (MXVCS). Theoretical analysis on the proposed constructions is provided. Extensive experiments and comparisons are conducted to illustrate that the pixel expansion, the visual quality of recovered image and the efficiency of generating base matrices are significantly improved by the proposed MXVCS, while comparing to MVCS and FIVCS.

PixelSteganalysis: Pixel-Wise Hidden Information Removal With Low Visual Degradation

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10–20 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in both the decoded rate and the DT.

A global progressive image secret sharing scheme under multi-group joint management.

Diverging from traditional secret sharing schemes, group secret sharing schemes enable the recovery of secret information through collaborative efforts among groups. Existing schemes seldom consider the issue of the secrecy level of image information between different groups. Therefore, we propose a global progressive image secret sharing scheme under multi-group joint management. For inter-group relations, multiple groups with different priority levels are constructed using the approach of bit-polar decomposition. In this arrangement, higher-level groups obtain clearer secret image information. For intra-group relations, a participant-weighted secret sharing scheme is constructed based on Chinese Remainder Theorem and Birkhoff interpolation, in which the participants' secret sub-shares are reusable. During the recovery process, the sub-images can be recovered within the intragroup with the corresponding level. Groups collaborate through lightweight overlay operations to obtain different layers of secret images, achieving a global progressive effect. Analysis results show that the scheme is both secure and practical for group secret sharing.

A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret image. We suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold transform. The notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in payload. By scrambling the cover image, Arnold transform adds security to the information that gets embedded and also allows embedding more information in each iteration. The hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure system. The proposed method employs a set of keys to ensure that information cannot be decoded by an attacker. The experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats, including medical images. Various performance metrics proves that the retrieved cover image and hidden image are both intact. This System is proven to withstand rotation attack as well.

The Review of Secret Image Sharing

Secret image sharing (SIS) is a significant research topic of image information hiding, which divides the image into multiple shares and distributes them to multiple parties for management and preservation. In order to reconstruct the original image, a subset with predetermined number of shares is needed. And just because it is not necessary to use all of the shares to make a reconstruction, SIS creates a high <i></i> which breaks the limitations of traditional image protection methods, but at the same time, it causes a reduce of safety. Recently, new technologies, such as deep learning and blockchain, have been applied into SIS to improve its <i></i> and <i></i>. This paper gives an overall review of SIS, discusses four important approaches for SIS, and makes a comparison analysis among them from the perspectives of pixel expansion, tamper resistance, etc. At the end, this paper indicates the possible research directions of SIS in the future.

Securing image-based document transmission in logistics and supply chain management through cheating-resistant visual cryptographic protocols.

In today's digital landscape, securing multimedia visual information-specifically color images-is of critical importance across a range of sectors, including the burgeoning fields of logistics and supply chain management. Traditional Visual Cryptography (VC) schemes lay the groundwork for encrypting visual data by fragmenting a secret image into multiple shares, thereby ensuring no single share divulges the secret. Nevertheless, VC faces challenges in ascertaining the integrity of reconstructed images, especially when shares are manipulated maliciously. Existing solutions often necessitate additional shares or a trusted third party for integrity verification, thereby adding complexity and potential security risks. This paper introduces a novel Cheating-Resistant Visual Cryptographic Protocol (CRVC) for Color Images that aims to address these limitations. Utilizing self-computational models, this enhanced protocol simplifies the integrated integrity verification process, eliminating the need for extra shares. A standout feature is its capability to securely transmit meaningful shares for color images without compromising the quality of the reconstructed image as the PSNR maintains to be ∞. Experimental findings substantiate the protocol's resilience against quality degradation and its effectiveness in verifying the authenticity of the reconstructed image. This innovative approach holds promise for a wide array of applications, notably in sectors requiring secure document transmission, such as Logistics and Supply Chain Management, E-Governance, Medical and Military Applications.

Deep Neural Network-based Reversible Image Steganography Technique using Circle-U-Net

Data communication is made at the ease with the advent of the latest communications medium and tools. The concern over data breaches has increased. The digital media communicated across the network are susceptible to unapproved access. Though numerous image steganography approaches were existing for concealing the secret image into the cover image there are still limitations such as inadequate restoration of image quality and less embedding capacity. To overwhelm such shortcomings recently many image steganography approaches based on deep learning are proposed. In this work, a Circle-U-Net-based reversible image steganography technique is proposed. The model includes a contracting process, which includes residual bottleneck as well as circle connect layers which obtain context; an expanding process, which includes sampling layers as well as merging layers for pixel-wise localization. The reversible image steganography (RIS) is carried out with neural network models such as CNN, U-Net scheme, and Circle-U-Net structure on TinyImageNet-200 and Alzheimer’s MRI dataset. The proposed technique is experimented along with RIS using CNN and RIS using U-Net. The experimental results depict that the RIS using the Circle-U-Net structure performs better among the three models.

Robust Steganography Technique for Embedding Secret Images

Steganography is a technique used to disguise the existence of a secret communication. It is used in many fields to solve information security problems. Steganography is a technique to embed secret data in a carrier image and obtain a new image that can't actually be distinguished from the original image. This paper proposes steganography method based on least significant bit (LSB) replacement and integer wavelet transform IWT through lifting scheme to achieve high quality of stego image. we will do some pre-processing on the secret image before embedding process. IWT transforms the secret image from spatial domain to a frequency domain and will be divided it into a group of sub-bands, some of which we will use for their utmost importance and ignore some of them.&#x0D; We have embedded the secret image in a sequential LSB method and in a randomly LSB method and also by the method LSB matching. But after applying our proposed method to the secret image and then embedded it in each of the three above-mentioned methods, it was found that it had a higher degree of imperceptibly and obtained a higher rate of PSNR and the possibility of recovering the secret image without errors. By using the randomly and LSBM methods gives a higher security and resistance to extraction by attackers.

Hybrid Hyper Chaotic Map with LSB for Image Encryption and Decryption

There are number of images that transmitted through the web for various usages like medical imaging, satellite images, military database, broadcasting, confidential enterprise, banking, etc. Thus, it is important to protect the images confidentially by securing sensitive information from an intruder. The present research work proposes a Hybrid Hyper Chaotic Mapping that considers a3D face Mesh model for hiding the secret image. The model has a larger range of chaotic parameters which are helpful in the chaotification approaches. The proposed system provides excellent security for the secret image through the process of encryption and decryption. The encryption of the secret image is performed by using chaos encryption with hyper hybrid mapping. The hyper hybrid mapping includes enhanced logistic and henon mapping to improve the computation efficiency for security to enhance embedding capacity. In the experiment Fingerprint and satellite image is used as secret image. The secret image is encrypted using a Least Significant Bit (LSB) for embedding an image. The results obtained by the proposed method showed better enhancements in terms of SNR for the 3D Mesh model dataset as 77.85 dB better compared to the existing models that achieved Reversible data hiding in the encrypted domain (RDH-ED) of 33.89 dB and Multiple Most Significant Bit (Multi-MSB) 40 dB. Also, the results obtained by the proposed Hybrid Hyper chaotic mapping showed PSNR of 65.73 dB better when compared to the existing Permutation Substitution and Boolean Operation that obtained 21.19 dB and 21.27 dB for the Deoxyribonucleic Acid (DNA) level permutation-based logistic map.

Secret image sharing by using multi-prime modular arithmetic

All secret image sharing (SIS) schemes are based on a finite field, on which the generation and reconstruction can be accomplished correctly. To achieve distortionless secret images, a Galois field GF(2m) would be adopted with increased computational complexity. Simple modular arithmetic, on the other hand, is frequently utilized to improve computational efficiency. However, the SIS using simple modular arithmetic is not a completely distortion-free scheme. In this paper, simple modular arithmetic will continue to be investigated to reserve its utility of reducing computational complexity. Multi-prime modular arithmetic is explored for building SIS with multi-prime (referred to as SISw/M) rather than using single-prime modular arithmetic. When dealing with the N-bit frames in a secret image, the prime employed in SISw/M would be closer to the 2N value, resulting in improved image quality. In addition, with Chinese Remainder Theorem, progressive recovery is provided. Experimental results and comparisons are demonstrated to show the merits (enhanced image quality and progressive recovery) of the proposed approach.

Hybrid information security system via combination of compression, cryptography, and image steganography

&lt;span lang="EN-US"&gt;Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.&lt;/span&gt;

Implementasi Algoritma RC4A Dalam Pengamanan Citra Digital

Digital images that are private and confidential are very vulnerable to wiretapping by irresponsible parties, especially if the images are distributed via the internet. The act of theft and abuse of secret images of course can harm the image owner. One of the techniques to reduce the above problems is by using cryptographic techniques. Cryptographic techniques can secure images by means of digital image encryption by implementing the RC4A algorithm then using the initial keyword as input in the process carried out to generate new, more random keywords, based on the Key Schedule Algorithm. (KSA) and pseudo random generation algorithm (PRGA). To determine the comparison between the original image (PlainImage) and the original image (CipherImage), processing was carried out with MeanSquareError (MSE) and PeakSiqnalToNoiseRation (PSNR). The results of this study were digital images encrypted with the RC4A algorithm in terms of time differences and image results after encryption where the time used to encrypt is shorter than the description and the image results before encryption are not the same after encryption.

SABMIS: sparse approximation based blind multi-image steganography scheme.

We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet.

Generative Text Secret Sharing with Topic-Controlled Shadows

Secret image sharing has been extensively and thoroughly researched. However, in the social network environment, shadow images are subject to compression or noise pollution during uploading and transmitting, which makes it challenging to recover secrets losslessly. Texts are more suited for transmission in social networks as shadows because of the broad variety of application scenarios and inherent robustness. Through a secret sharing technique of k , n threshold, a secret is encrypted as n shadows, where any k or more shadows can recover the secret, while less than k cannot obtain any information on the secret. In this article, we propose a generative text secret sharing scheme with topic-controlled shadows, which encrypts a secret message as a number of semantically natural shadow texts and controls the topics of shadow texts using bag-of-words models during text generation by the language model. This study also proposes two goal programming models to improve the shadow texts’ topic relevance and fluency. The shadow texts of the proposed scheme satisfy loss tolerance, semantic comprehensibility, topic controllability, and robustness. An ablation study, comparative test, and anti-detection experiment verify the effectiveness of the proposed scheme.

FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter

In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).

High-capacity coverless image steganographic scheme based on image synthesis

In this paper, a coverless image steganographic scheme with high capacity is proposed. It can hide a color secret image into a color stego image with the same size. Furthermore, the stego image is synthesized without the aid of any cover images. Since there are no cover modification operations, it reduces the risk of being detected by steganalysis tools. The proposed scheme comprises a stego image synthesis sub-network (SynthesisNet) and a secret image recovery sub-network (RevealNet). In SynthesisNet, we apply the image-to-image translation to implement cross-domain image synthesis. It accepts source and reference images as the inputs, which can be used to customize the semantic and style of the stego image. A mapping between source and reference images is also designed to enhance the authenticity of the stego images. Experiments show that the proposed scheme provides high steganographic undetectability as well as good quality on stego and recovered secret images. Moreover, it can generate stego images with specified contents different from any existing images in the public channel.

Stokes meta-hologram toward optical cryptography

Optical cryptography manifests itself a powerful platform for information security, which involves encrypting secret images into visual patterns. Recently, encryption schemes demonstrated on metasurface platform have revolutionized optical cryptography, as the versatile design concept allows for unrestrained creativity. Despite rapid progresses, most efforts focus on the functionalities of cryptography rather than addressing performance issues, such as deep security, information capacity, and reconstruction quality. Here, we develop an optical encryption scheme by integrating visual cryptography with metasurface-assisted pattern masking, referred to as Stokes meta-hologram. Based on spatially structured polarization pattern masking, Stokes meta-hologram allows multichannel vectorial encryption to mask multiple secret images into unrecognizable visual patterns, and retrieve them following Stokes vector analysis. Further, an asymmetric encryption scheme based on Stokes vector rotation transformation is proposed to settle the inherent problem of the need to share the key in symmetric encryption. Our results show that Stokes meta-hologram can achieve optical cryptography with effectively improved security, and thereby paves a promising pathway toward optical and quantum security, optical communications, and anticounterfeiting.

Reversible data hiding in encrypted image with local-correlation-based classification and adaptive encoding strategy

In recent years, reversible data hiding in encrypted images has attracted more and more attention due to its wide application in secret information transmission and image content protection. In this paper, we propose a reversible data hiding scheme for encrypted image based on classification and multi-layer processing. The image owner utilizes a block-level encryption method, including block-level bitwise exclusive-or operation and block permutation, to obtain encrypted image such that the correlation of pixels in most blocks is preserved. Therefore, there provably exist identical consecutive high-order bit planes (ICHOBPs) from the most significant bit (MSB) where the number of ICHOBPs is defined as local correlation. Further, a local-correlation-based classification mechanism is proposed such that these blocks can be classified into different types. The data hider adopts multi-layer processing to fully exploit the local correlation in each block. First, ICHOBPs of each block are utilized as redundant space for data embedding. For those blocks with a small number of ICHOBPs, an adaptive coding strategy is proposed to improve embedding capacity. Especially for those blocks whose correlation is not well preserved, we propose a special processing method to exploit redundant space. Experimental results show that our method can effectively improve the embedding capacity than other similar ones with keeping high quality in security analysis.