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This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) for military as well as civilian applications such as search and rescue operations, disaster management, parcel delivery and agriculture, is becoming increasingly popular, mainly due to their versatile nature and relatively inexpensive operating costs. However, one of the most significant barriers in the widespread adoption of UAVs for the aforementioned applications is the increasing concern for wireless communication security within the network. Unfortunately, the nodes that comprise these networks are extremely constrained in terms of storage space, processing power and battery life, which require light-weight security protocols that align with these limitations. The current standards for message authentication and verification predominantly rely on lightweight cryptographic techniques. However, there is continual scope for improvement, particularly in the realm of computational efficiency, to better align with the constraints inherent to UAVs. This paper explores data hiding in a multi-UAV environment, to form a light-weight message verification scheme to confirm the identity of the transmitting node. First, five venues having the potential to hide data are analyzed. Based on the analysis of bit error rate obtained from the simulated multi-UAV environment, both cyclic prefix and padding bits are selected. Next, a codeword is generated for each transmission and it is embedded along with the unique ID key, κ of the transmitting node into each venue respectively, and the output is transmitted as Hello packets. Subsequently, the receiving node extracts and decodes the codeword in order to verify the authenticity of the transmitting node. An evaluation of the proposed scheme has been conducted by using a simulated UAV environment. In the best case scenario, the proposed authentication scheme can achieve a successful verification rate of at least 80% at a maximum hop-count of 10 hops. Through computational cost analysis, in comparison to the conventional methods, the proposed scheme was found to have a significantly lower total execution time of 1.7 μ s . In addition, the security analysis shows that when the proposed scheme is paired with physical unclonable functions (PUFs), it is able to resist common security attacks, including man-in-the-middle, replay and cloning attacks. [ABSTRACT FROM AUTHOR]

Video steganography approach has been widely used to preserve the secret information while transmitting through the internet. This approach has become attractive among many researchers due to its feature high Embedding Capacity (EC). In recent years, numerous methods are developed to secure the data. However, these methods still face problem in the area of imperceptibly and finding less distortion regions that is suitable for data hiding. To overcome this issue, a novel edge based Moving Objects (MO), compression technology, encryption method and interpolation data hiding technique based on moving regions detection that achieves less distortion is proposed in this paper. In the proposed system, first the video gets separated into frames, and edge based MO detection technique with graph system is used to identify the missing vertices. Then, compression technology with Rank Approximation (RA) and Prediction Error (PE) is applied for every frame. Next, we propose encryption scheme for MO which combines threshold and Pixel Value Differencing (PVD) to shuffle pixels, and non-MO applies circular shift encryption that preserve the pixels suitable for data hiding. Finally, the frames are up-scaled using weight based interpolation technique and data is hidden with various range inside Red Green Blue (RGB) channel by finding rough and smooth pixels. To ensure reversibility, any one channel is kept as the indication pixel for each pixel to store MO pixels. Experimental results reveals that the proposed method has significant improvement in the areas of compression level, MO identification and Quality Parameter (QP) testing. Moreover, Peak Signal-to-Noise Ratio (PSNR), EC, Regular Singular (RS) and PVD histogram analysis attacks obtained on the proposed method performs better than the other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Visualization of numerical results in computer communications is very important such that some very small differences are sometimes crucial, distinguishable, and descriptive for comparison among some state-of-the-art techniques. For the issue of data quality evaluation and compression rates in internet of multimedia things, there are many metrics traditionally, for instance, peak signal-to-noise ratio (PSNR) is strongly able to describe non-sensitive (and relatively ambiguous) results of mean square error and since PSNR is normally between 10 and 100 for most of the lossy techniques, it can plotted with using any graphical/visualization tool. However, the results of compression rates for aggregation techniques may be a little complicated on which using a non-flexible mathematical operator like logarithm may have an unsuitable effect with ignoring the small differences while plotting the results. The aim behind this paper is to introduce a new metric entitled average capacity index (ACI), as a non-linear visualization approach/scaling mechanism, to be usable in evaluating capacity results of data hiding and aggregation algorithms based on bar charts. Some examples with synthetic and real data will show that the proposed metric outperforms the existing conventional tools in terms of statistical measures and visual presentation. [ABSTRACT FROM AUTHOR]

Recently, a serious problem in communications is security. Hiding data is one of the most important security techniques. Steganography is the art and science of hiding information in a cover media. Texts are the most usual method of communication and so they are very suitable for cover objects. In this paper, we give a new technique for text steganography. There exist different models, such as RGB, HSL, HSV to determine a color. The main goal of the proposed algorithm is the fact that some different but very similar colors in RGB have the same code in HSL. We use this fact to hide data. For this purpose, first we find a color B′ in RGB, which is very similar to black in such a way that they have the same code in HSL. Then, by changing the color of each character of the text to black or B′, we conceal the information. We will show that the capacity of this method is better than some other methods of text steganography, and then we show that the invisibility of this algorithm is very high, which is the most prominent feature of the proposed technique. [ABSTRACT FROM AUTHOR]

Health developments have made it easier to transfer patient information although raising concerns about data vulnerability. In such scenarios, ensuring patient data security when transmitted and stored is essential. Steganography with the Prediction Error Expansion (PEE) can be a solution for storing critical patient data in ECG signals. However, PEE has shortcomings in balancing the quality and capacity of the embedding with the threshold used. Therefore, the method's improvisation utilizes a mirror embedding scheme in PEE with a regression predictor to decrease the disparity of prediction results. To evaluate the method, the experiment used datasets from ECG MITBIH. The results show that the resulting ECG signal can be maintained in a manner that is as similar as possible to the original signal. The quality of the embedding results can also be maintained above 52.8 dB for SNR and below 0.252 for PRD at high bps, which is higher than that of other PEE-based or newest ECG steganographic methods. The resulting algorithm shows an increasing speed of up to 22.7 times from the existing method. [ABSTRACT FROM AUTHOR]

In the face of rapid advancements in information transmission technology, ensuring the secure development of all application aspects is imperative, particularly considering the frequent data transmission over public networks. A significant vulnerability exists for data compromise during transmission without robust security measures. Thus, implementing a reliable data-hiding scheme becomes indispensable to safeguarding confidential information. Despite the introduction of various methods to address this concern, challenges persist in effectively managing data size and maintaining the resulting data quality. This research addresses these challenges with the key idea of using a combination of dynamic layering and sample interpolation to increase the samples' capacity in accommodating the secret bits. The novelty of this study is based on the dynamic use of the multi-layering paradigm during the data embedding, which contributes to maintaining the quality of the audio sample considered for concealment. The experimental results show a significant outperformance of the proposed method, which gets a maximum peak signalto- noise ratio (PSNR) of 120.39 decibels (dB), an ideal PSNR to secure secret data transmission. [ABSTRACT FROM AUTHOR]

Recently, a serious problem in communications is security. Hiding data is one of the most important security techniques. Steganography is the art and science of hiding information in a cover media. Texts are the most usual method of communication and so they are very suitable for cover objects. In this paper, we give a new technique for text steganography. There exist different models, such as RGB, HSL, HSV to determine a color. The main goal of the proposed algorithm is the fact that some different but very similar colors in RGB have the same code in HSL. We use this fact to hide data. For this purpose, first we find a color $B'$ in RGB, which is very similar to black in such a way that they have the same code in HSL. Then, by changing the color of each character of the text to black or $B'$, we conceal the information. We will show that the capacity of this method is better than some other methods of text steganography, and then we show that the invisibility of this algorithm is very high, which is the most prominent feature of the proposed technique.

Abstract In recent years, related research has focused on how to safely transfer and protect the privacy of images in social network services while providing easy access by authorized users. To safeguard privacy, we suggest an image encryption scheme that combines data hiding and image encryption. The proposed scheme successfully decrypts images after JPEG compression attacks and preserves the privacy of secret regions through the use of block scrambling encryption based on region selection. Simultaneously, the scheme can handle nonuniform secret regions and obtain more sensitive secret keys because of the incorporation of a chaotic system. The enhanced deep learning-based data-hiding technology reduces algorithm complexity by enabling the encryption position to be determined in the decryption phase without the need for any information or equipment. However, this approach also increases algorithm security, because only when the right secret data are extracted can they be decrypted successfully. According to the experimental findings, the proposed scheme can correctly decrypt images via JPEG compression while maintaining visually acceptable quality. The proposed scheme can achieve greater robustness against image processing algorithms and a wider secret key space than traditional schemes.

The exchange of information through social networking sites has become a major risk due to the possibility of obtaining millions of subscribers’ data at any time without the right. Multimedia security is a multifaceted field that involves various techniques and technologies to protect digital media in different contexts. As the technology evolves, so do the challenges and solutions related to multimedia security. Steganography plays a dominant role in covert communication over these social networking. In most modern adaptive steganography, the balancing between imperceptibility, payload, and security is a critical difficulty for image steganography. To this end, in this paper, we propose an improved image steganography method called IS-DGM based on a deep generative model (DGM) combined with hyper logistic map (HLM) encryption algorithm. IS-DGM consists of two strategies, steganography and recovery. In the first strategy, we have pre-processing and embedding networks. Before running the pre-processing network, the secret image is encoded using the HLM algorithm. During this phase, the encoded and the carrier images are utilized as inputs of the embedding network to boost concealment efficiency. In the second strategy, we have extraction and steganalysis networks. During this phase, the secret is extracted from the host image with the good visual quality as possible. Experimental outcomes indicate that the proposed method performs effectively in terms of perceptual quality and embedding capacity on five data sets, namely, ImageNet, CoCo2017, LFW, VoC2007, and VoC2012. In addition, it outperforms recent deep learning GAN hiding algorithms with respect to capacity, visual quality, and security. Thus, the proposed IS-DGM effectively balances good imperceptibility and increased capacity. Further, it maintains safety against histogram analysis, such as PVD analysis. Besides, the IS-DGM method increases resistance to the ROC curve analysis, including steganalysis algorithms, such as SRM, MaxSRM, Stegexpose, and Ye-Net. [ABSTRACT FROM AUTHOR]

In digital communications, the imperative for secure data transmission is increasingly addressed through steganography, wherein information is clandestinely embedded within various digital media. This study is concerned with the enhancement of steganographic techniques through a modified U-Net architecture, designed to embed 256×256 colored message images into identically sized cover images, thereby augmenting capacity for data concealment. The classical U-Net architecture has been adapted by the incorporation of batch normalization and residual blocks, aiming to refine the embedding and extraction processes's efficiency. The novel model, trained on the expansive ImageNet database, introduces the one cycle learning rate scheduler and the AdamW optimizer into the U-Net framework, achieving enhanced training efficiency, hastened convergence, and superior generalization. Validation was conducted through two distinct analyses: the first evaluating the impact of secret image size variations on the cover image within the steganographic process, and the second assessing model performance on three datasets—Linnaeus 5, ImageNet, and Labeled Faces in the Wild (LFW). Empirical assessments indicate that the proposed model outperforms existing deep learning-based steganographic methods, as evidenced by the attained metrics, particularly Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). On the Linnaeus 5 dataset, embedding yielded a PSNR of 44.4656 dB and an SSIM of 0.9897, while extraction recorded a PSNR of 43.5393 dB and an SSIM of 0.9875. The ImageNet dataset saw an embedding PSNR of 45.3966 dB and an SSIM of 0.9906, with extraction values of 44.8206 dB PSNR and 0.9903 SSIM. Notably, the LFW dataset embedding resulted in a PSNR of 48.1407 dB and an SSIM of 0.9930, and extraction achieved a PSNR of 47.5296 dB and an SSIM of 0.9907. The qualitative and quantitative outcomes affirm the efficacy of the proposed method for the secure transmission of confidential imagery, with potential applications ranging from the safeguarding of medical records to the protection of sensitive data across various digital platforms. [ABSTRACT FROM AUTHOR]

In recent years, data hiding techniques have emerged as a prominent area of research due to the increasing demand for information security. Existing data hiding techniques typically do not prioritize the verification of the correctness of the extracted data, nor do they attempt to rectify any erroneous information extracted. This paper presents a simple yet effective method by which a recipient can perform error detection and correction on extracted data. Prior to data embedding, a preprocess is applied to secret data. After converting the secret data to a data stream, a data hider categorizes adjacent and identical bits within the data stream into groups. The number of these bits is then recorded as an indicator to extract information and perform error detection and correction. The number of bits in each group is adjusted to ensure that it is an odd number. Moreover, the collected indicator data stream is encoded using (7,4) Hamming code to compact the recorded information. Finally, the concatenation of the encoded indicator and the encoded secret is embedded into the multiple least significant bits (LSBs) of the original image pixels. As for the receiver, error correction can be applied based on the indicator after the extraction of the encoded data. Experimental results demonstrate that this method allows the data receiver to detect and correct errors while maintaining a certain level of embedding capacity and image quality. In terms of image quality, this method exhibits superior performance compared to existing research when the embedded data volume is relatively small. [ABSTRACT FROM AUTHOR]

This paper presents a novel financial text image security authentication algorithm based on binary wavelet transform. Initially, the algorithm preprocesses the original financial text image using binary wavelet transform. Subsequently, it categorizes the pixels in each low-frequency subband image block based on their flippability. The "nonflippable" pixels are then hashed to generate watermark information, which replaces the "flappable" pixels in the corresponding mapping block to embed the watermark into the low-frequency subband. By studying the characteristics of high-frequency coefficients, the algorithm identifies the flippable coefficients in the frequency domain and uses block mapping to embed high-frequency subband encryption watermark information. The authenticity of the image block is verified by comparing the consistency between the reconstructed watermark and the extracted watermark. Furthermore, the "low-frequency-high-frequency subband joint judgment criterion" is employed to enhance tampering detection performance. Our experimental results indicate that the structural similarity (SSIM) of financial text images embedded with watermarks using this algorithm is above 0.99, satisfying the human visual system's image quality requirements. Under different tampering attack rates, the missed-detection probability and false-detection probability of this algorithm are lower than those of existing methods. It is also compatible with financial text image compression algorithms, making it suitable for tampering detection of important financial text images. [ABSTRACT FROM AUTHOR]

With the expansion of IOTs and Wireless devices, data transmission has increased over the internet. Maintaining security during data transmission using invisible watermarks has become a focus and has captured the attention of numerous researchers. The three security necessities, privacy, integrity, and authenticity, call for close attention and remedial actions. In this respect, watermarking and cryptographic techniques are a probable way to handle the above-mentioned issues adequately. The paper presents a novel approach to watermarking using a combination of NSST, HD, and SVD transforms, which improves the robustness and security of the watermarked image. The method involves using a grayscale image as the watermark and embedding it in multiple cover images. The resulting images are transformed sequentially using NSST, HD, and SVD to obtain the final watermarked images. The authors also apply AES encryption to enhance security, which generates a hash subkey and authentication tag. The proposed methodology performs better than other watermarking techniques, with a maximum improvement of 47.81%. Overall, the proposed approach offers a more secure and robust solution for watermarking. [ABSTRACT FROM AUTHOR]

The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration. [ABSTRACT FROM AUTHOR]

In recent decades, information security has become increasingly valued, including many aspects of privacy protection, copyright protection, and digital forensics. Therefore, many data hiding schemes have been proposed and applied to various carriers such as text, images, audio, and videos. Vector Quantization (VQ) compression is a well-known method for compressing images. In previous research, most methods related to VQ compressed images have focused on hiding information in index tables, while only a few of the latest studies have explored embedding data in codebooks. We propose a data hiding scheme for VQ codebooks. With our approach, a sender XORs most of the pixel values in a codebook and then applies a threshold to control data embedding. The auxiliary information generated during this process is embedded alongside secret data in the index reordering phase. Upon receiving the stego codebook and the reordered index table, the recipient can extract the data and reconstruct the VQ-compressed image using the reverse process. Experimental results demonstrate that our scheme significantly improves embedding capacity compared to the most recent codebook-based methods. Specifically, we observe an improvement rate of 223.66% in a small codebook of size 64 and an improvement rate of 85.19% in a codebook of size 1024. [ABSTRACT FROM AUTHOR]

This paper proposes an extended method for reversible color tone control for blue and red tones. Our previous method has an issue in that there are cases where the intensity of enhancement cannot be flexibly controlled. In contrast, the proposed method can gradually improve the intensity by increasing the correction coefficients, regardless of the image features. This is because the method defines one reference area where the correction coefficients are determined, one each for blue and red tones, while the previous method defines a common reference area for both tones. Owing to this, the method also provides independent control for blue and red tones. In our experiments, we clarify the above advantages of the method. Additionally, we also discuss the influence of the data-embedding process, which is necessary to store recovery information, on the output image quality. [ABSTRACT FROM AUTHOR]

Steganalysis, a field devoted to detecting concealed information in various forms of digital media, including text, images, audio, and video files, has evolved significantly over time. This evolution aims to improve the accuracy of revealing potential hidden data. Traditional machine learning approaches, such as support vector machines (SVM) and ensemble classifiers (ECs), were previously employed in steganalysis. However, they demonstrated ineffective against contemporary and prevalent steganographic methods. The field of steganalysis has experienced noteworthy advancements by transitioning from traditional machine learning methods to deep learning techniques, resulting in superior outcomes. More specifically, deep learning-based steganalysis approaches exhibit rapid detection of steganographic payloads and demonstrate remarkable accuracy and efficiency across a spectrum of modern steganographic algorithms. This paper is dedicated to investigating recent developments in deep learning-based steganalysis schemes, exploring their evolution, and conducting a thorough analysis of the techniques incorporated in these schemes. Furthermore, the research aims to delve into the current trends in steganalysis, explicitly focusing on digital image steganography. By examining the latest techniques and methodologies, this work contributes to an enhanced understanding of the evolving landscape of steganalysis, shedding light on the advancements achieved through deep learning-based approaches.

This article proposes a novel double data security algorithm that first encrypts sensitive data using a two-stage encryption method based on numerical solutions from a fractional-order memristive coupled neural network system. Solutions are obtained to generate encryption keys and construct S-boxes, which are then applied along with an initial key to encrypt the data bits through repeated XOR and S-box operations. The encrypted output is then hidden imperceptibly within 3D geometries by slightly modifying model points based on the encrypted data bits. This two-pronged approach provides enhanced protection for confidential information compared to single encryption or data hiding alone. Numerical experiments demonstrate the effectiveness of encryption in obscuring patterns while data extraction from modified 3D models validates recovery with negligible visual impact. Additionally, the proposed encryption scheme is shown to be superior to the standard AES-256 algorithm in terms of both computational efficiency and security against brute-force attacks. Through a synergistic blend of robust encryption and stealthy data hiding within 3D objects, the presented algorithm can reliably ensure privacy for sensitive digital data transmissions and storage applications.

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image’s Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patient’s privacy and data integrity.

To protect the security of medical images and to improve the embedding ability of data in encrypted medical images, this paper proposes a permutation ordered binary (POB) number system-based hiding and authentication scheme for medical images, which includes three parts: image preprocessing, double hiding, and information extraction and lossless recovery. In the image preprocessing and double hiding phase, firstly, the region of significance (ROS) of the original medical image is segmented into a region of interest (ROI) and a region of non-interest (RONI). Then, the bit plane of the ROI and RONI are separated and cross-reorganization to obtain two new Share images. After the two new Share images are compressed, the images are encrypted to generate two encrypted shares. Finally, the embedding of secret data and attaching of authentication bits in each of these two encrypted shares was performed using the POB algorithm. In the information extraction and lossless recovery phase, the POBN algorithm is first used to extract the authentication bits to realize image tamper detection; then, the embedded secret message is extracted, and the original medical image is recovered. The method proposed in this research performs better in data embedding and lossless recovery, as demonstrated by experiments. [ABSTRACT FROM AUTHOR]

In tandem with the fast-growing technology, the issue of secure data transmission over the Internet has achieved increasing importance. In digital media, enclosing data in images is one of the most common methods for communicating confidential information. A novel reversible data hiding in the encrypted images scheme based on selective bin models is proposed in this paper. The scheme focuses on enhancing the embedding capacity while ensuring the security of images with the help of encryption and the proposed data hiding process. For data embedding, lossless compression is utilized and the image is classified into three bins. Then, marker bits are assigned to these bins for distinguishing between embeddable and non-embeddable regions. The proposed method shows a satisfactory embedding rate for smooth images as well as complex ones due to its selective bin approach. Also, the method is separable in nature, i.e., data extraction and image recovery can be performed independently. Furthermore, the experimental results demonstrate the strategy’s effectiveness when compared with others. [ABSTRACT FROM AUTHOR]

Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics, which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram. Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases. [ABSTRACT FROM AUTHOR]

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images. [ABSTRACT FROM AUTHOR]

Steganography--the practice of hiding data in digital media--has attracted a lot of interest because of its possible uses in secure communication. This study offers a thorough summary of the most recent developments in steganography, emphasizing the incorporation of deep learning (DL) methods in particular. The review explores several steganographic techniques, classifies them, and assesses the advantages and disadvantages of each. Moreover, it reviews current research projects that use DL to improve security and resilience in steganographic systems. This evaluation finds new trends, problems, and possibilities in the field by examining related studies. The major goal is to advance this field and broaden our understanding of steganography-based secure communication. [ABSTRACT FROM AUTHOR]

Data hiding along with the security plays a vital role in the wireless network. In recent years, object detection, Feature Extraction (FE) and correlation based technique are developed to predict the pixels that is suitable for embedding. However, traditional data hiding method still suffered in the selection of less distortion pixels. To overcome these shortcomings, this method proposes (i) Moving Object (MO) detection, (ii) FE, (iii) data hiding, and (iv) data encryption. Initially, the MO in the video are detected using optical flow and tracking the weight of MO. Next, data hiding features are extracted using Optimization Fruit fly Algorithm (OFA) with Differential Evolution (DE) and Opposition Based Learning (OBL) algorithm. Then, a modified data hiding techniques based on correlation, regression and pixel segmentation with Prediction Error (PE) techniques are used for improving the performance. Finally, the hidden data is encrypted using Binary Tree Structure (BTS) to provide additional layer of security. Experimental result shows the proposed method is superior to other state-of-the-art methods in terms of Peak-Signal-to-Noise Ratio (PSNR), embedding capacity and security analysis. [ABSTRACT FROM AUTHOR]

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner. [ABSTRACT FROM AUTHOR]

Due to the rapid development of information technology, the process of exchanging information over the web has become simple and fast. Nevertheless, the transmission of information will be at risk due to the nature of the internet, as anyone can access and change the data by hacking it. Therefore, it is necessary for protecting any personal information from being accessed by unlawful individuals. It became necessary to use encryption and steganography techniques for protecting data. Steganography is a manner used for covering hidden information in several other media while preserving the form of the original data and leaving no evidence of hidden data, whereas, encryption changes the original information into unclear or ambiguous information, which is called scrambled. The paper presents a method of video steganography as a powerful and effective tool for data steganography. We used a video as a spreading medium and then used it for concealment of an audio message and a document (in a pdf file) in a method that makes the information invisible using the least significant bit (LSB) method. [ABSTRACT FROM AUTHOR]

Compression techniques can be used on digital content to minimize duplication and maintain internet traffic, while ensuring the quality of the decoded object. Two functionalities such as data concealing and images compression can be combined into a single module, reducing the risk of responder attacks while improving execution efficacy. This study exploits Block Truncation Coding (BTC) with (7, 4) Hamming error correction and detection code to render a robust data hiding policy within highly compressed images. To compress the host image, BTC has been utilized which divides the image into (4 × 4) blocks. These blocks are categorized into two groups namely complex and smooth, based on threshold value where each smooth block accommodate two secret data bits encoded in it employing (7, 4) Hamming code whereas complex block remain unaltered. On the other hand, the Vertical Redundancy Check (VRC) approach greatly aids in determining the location of the hidden message bit. The cover or host image can be partially recovered due to the lossy property of BTC image compression scheme. To test its robustness and imperceptibility, several standard NIST approved steganalysis and statistical attacks are carried out. The developed technique is found to be secure and strong against a variety of geometrical attacks. The virtues of the proposed scheme are presented by comparing experimental results to the state-of-the-art schemes. The anticipated consequence highlighted certain outstanding magnificent aspects in the fields of verification of image, tamper recognition, and digital forgery sensing, all of which are essential to the contemporary technological life. This scheme benefits a wide range of government and business sectors, as well as health care, security, intellectual property rights, commercial and defense. [ABSTRACT FROM AUTHOR]

Hiding a data inside video plays a vital role in data communication. The aim of data hiding is to protect the data with minimum distortion rate. In this paper, key frame selection data hiding in videos using Search Tree Labeling Scheme (STLS) is proposed. The cover video in the frames is trained for noise removal and compression using Denoising Convolution Neural Network (DnCNN) and the quality factor is checked for every frame using parameters. The compression is applied and key frames in the cover video are selected using Neural Image Assessment (NIMA) and Convolution Neural Network (CNN) model. This model is trained to predict the score of each frame based on quality metrics. The high score frame are selected as the key frame and it is separated into 2 × 3 blocks. Each block of pixels are represented in the tree structure using STLS to identify the differences and pixel is named in the tree according to its distortion range. Data is embedded using odd or even bit embedding on the pixel using Most Significant Bit (MSB) and Least Significant Bit (LSB). The experimental result reveals that the proposed schemes can attain better data extraction with more Embedding Capacity (EC), and security. The proposed method provides Peak Signal Noise Ratio (PSNR) value for ImageNet VID is 46.63 and CD-net 2014 is 45.47 and EC has achieved for ImageNet VID: 693,242 and CD-net 2014: 712,422 bits). Moreover the proposed scheme is significantly outperforms the state-of-the-art techniques in terms of quality assessment in the selection of frames and data embedding. [ABSTRACT FROM AUTHOR]

Steganography and Steganalysis are becoming increasingly relevant in information forensics and hiding data in the higher bitplanes without keeping any perceptible signature into the image is a challenging problem in this area. In this paper, we propose a unique solution to this problem using Fibonacci numbers as base. The pixels are selected from the busy part of the image where noticeable changes in pixel intensities occur. The business of the pixels is determined by their Fibonacci energy. The pixels values are converted into Fibonacci base and their corresponding Fibonacci energies are estimated by the Fibonacci expansion of pixel intensities. The set of energetic pixels are considered according to the descending order of their energy values. The binary data are concealed into higher bitplanes (up to 5) of the Fibonacci base of the pixel intensities. We theoretically derive some nice combinatorial properties related to distortion of pixel intensities and also experimentally show that our algorithm withstands against visual, structural and statistical attacks. The average embedding capacity is 3.98 bpp and average PSNR is 39.59 dB. We also demonstrate that our method is capable of resisting from the series of benchmark tests provided by StirMark 4.0. [ABSTRACT FROM AUTHOR]

Data hiding encompasses a wide range of applications related to hiding messages in digital images. The visual redundancy of images makes it possible to embed data in the images without attracting attention. Increasing the hiding capacity and decreasing the hiding distortion are prime objectives that data hiding intends to achieve. In this paper, we propose a high-payload data hiding scheme based on absolute moment block truncation coding (AMBTC) to minimize the impact of hiding. A two-level minimum mean square error (MMSE) quantizer generated by AMBTC is used to decrease the distortion associated with hiding. Also, we present a lookup table based on the symmetric property for adaptively hiding secrets in pixels to achieve high hiding capacity. We can embed almost 1.9 bits per pixel (bpp) with a high image quality of an average of 31 dB. Only 5.3% of pixels are changed during the data-hiding process. Compared with other schemes, we can use 1 bpp more relative payload for embedding with the same stego image quality. The experimental results show that the proposed scheme has better hiding performance because it allows a huge amount of secret data to be hidden while maintaining the high visual quality of the stego image. [ABSTRACT FROM AUTHOR]

Information has been uploaded and downloaded through the Internet, day in and day out, ever since we immersed ourselves in the Internet. Data security has become an area demanding high attention, and one of the most efficient techniques for protecting data is data hiding. In recent studies, it has been shown that the indices of a codebook can be reordered to hide secret bits. The hiding capacity of the codeword index reordering scheme increases when the size of the codebook increases. Since the codewords in the codebook are not modified, the visual performance of compressed images is retained. We propose a novel scheme making use of the fundamental principle of the codeword index reordering technique to hide secret data in encrypted images. By observing our experimental results, we can see that the obtained embedding capacity of 197,888 is larger than other state-of-the-art schemes. Secret data can be extracted when a receiver owns a data hiding key, and the image can be recovered when a receiver owns an encryption key. [ABSTRACT FROM AUTHOR]

Digital watermarking plays a vital role in protecting the rights and integrity of images, preventing unauthorized use, and promoting a safer and more accountable digital environment. Conventional watermarking schemes often suffer from limited robustness and susceptibility to various image processing operations, while deep learning-based watermarking schemes provide enhanced resilience to attacks and greater adaptability to complex image transformations, making them more effective in maintaining the integrity and ownership of digital content. This paper provides a concise analysis of commonly utilized deep learning networks and watermarking methods. The article begins by discussing the general framework, applications, and classification of watermarking based on different use cases. It then delves into deep learning network models such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Deep Neural Networks (DNN), along with associated watermarking techniques. Lastly, the paper addresses the challenges and offers guidance for future developers and researchers in this field. [ABSTRACT FROM AUTHOR]

This paper delves into the realm of steganography, focusing on the evolution of techniques employed to conceal data within digital images. Beginning with an exploration of the motivations behind information security and the role of cryptography, the paper introduces adaptive steganography as a discreet means of incorporating private data into a cover medium. The discussion then shifts to the three key factors in image steganography - resilience, capacity, and imperceptibility - forming the foundation of effective data hiding. The spatial and frequency domain methods are compared, with specific emphasis on the classical Least Significant Bit insertion and more advanced adaptive steganography techniques. The paper further introduces edge-based steganography, highlighting the advantage of manipulating edge areas for increased imperceptibility. Moving beyond definitions and types, the paper provides a comprehensive analysis of notable research works in the field, elucidating their objectives, methodologies, results, strengths, and limitations. The conclusion reflects on the dynamic landscape of steganography, acknowledging both achievements and areas for improvement. [ABSTRACT FROM AUTHOR]

This paper presents an optimal weighted modulus (OWM) algorithm able to conceal secret messages in a high dynamic range image encoded via the RGBE format, consisting of the red, green, blue, and exponent channels. In contrast to current state-of-the-art schemes, which mainly employ limited and vulnerable homogeneous representations, our OWM scheme exploits four channels and an embedding weight to conceal secret messages, thereby offering more embedding capacities and undetectability against steganalytic tools. To reduce the impact on the luminance variation, we confine the maximal change incurred in the exponent channel when embedding secret messages. In addition, we propose an SEC scheme to eliminate the pixel saturation problem, even though a pixel contains values close to the boundary extreme. As a result, the stego images produced not only exhibit high quality but also comply with the RGBE encoding format, making them able to resist malicious steganalytic detection. The experimental results show that our scheme offers larger embedding rates, between 2.8074 and 5.7549 bits per pixel, and the average PSNR value for twelve tone-mapped images is over 48 dB. In addition, the HDR VDP 3.0 metric demonstrates the high fidelity of stego HDR images, where the average Q value is close to the upper bound of 10.0. Our scheme can defeat RS steganalytic attacks and resist image compatibility attacks. A comparison result confirms that our scheme outperforms six current state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Anti-forensics techniques, particularly steganography and cryptography, have become increasingly pressing issues affecting current digital forensics practices. This paper advances the automation of hidden evidence extraction in audio files by proposing a novel multi-approach method. This method facilitates the correlation between unprocessed artefacts, indexed and live forensics analysis, and traditional steganographic and cryptographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation in detecting and extracting hidden artefacts in WAV and MP3 audio files. This comparison is made against standard industry systems. This work advances the current automation in extracting evidence hidden by cryptographic and steganographic techniques during forensic investigations. The proposed multi-approach demonstrates a clear enhancement in terms of coverage and accuracy, notably on large audio files (MP3 and WAV), where manual forensic analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present). However, it strikes a good balance between efficiently and effectively detecting hidden evidence, minimising false negatives and validating its reliability.

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved.