Hidden Secret Information Research Articles

Watermarking and Encryption Technique to Avoid Data Leakage in Cloud Storage with Block Chain Technology

Multimedia data sharing is increasingly becoming an essential part of the daily life for end users to access different systems, services, and applications. Data disclosure frequently occurs in real-world cloud storage services. Authentication and copyright protection of multimedia contents has always been a concern in secure data transfer media. The problem has become more critical with the increasing use of the Internet and digital technologies. However, making the protection of copyright is more complex and difficult. In proposed approach both Watermarking and Proxy Re-encryption (PRE) approach utilized for efficient multimedia content sharing. Watermarking is used to hiding the information such as hide secret information in digital media like images. Encryption techniques used to provide security to data. In proposed work, secret key can be encrypted using encryption algorithm with the help of key. Then user’s private key combined with encrypted key information and can be embedded within the image or audio using LSB (Least Significant Bit). After embedding of secret information, image or audio can be encrypting using ECC Encryption algorithm. Finally, authorized user can extract decryption key with the help of embedded data verification process. Unauthorized or illegal access can identify, when user information does not match with embedded information. And also secure these transaction details using Block chain technology.

Protecting health data in the cloud through steganography: A table-driven, blind method using neural networks and bit-shuffling algorithm

Securing health data stored in the cloud, including DNA sequences, patient information, and treatment histories, has become increasingly difficult due to modern cyber threats. To address this challenge, one approach is to employ steganography, a method of concealing sensitive information by merging it with other data. Recent studies have explored hiding secret information within DNA sequences, but these approaches have drawbacks. They either fail to generate a suitable cover from the modified sequence, known as the stego DNA sequence, or result in a significant increase in data that needs to be added to the original DNA sequence. Some methods even inadvertently include the original secret information or cover within the stego DNA sequence, creating security risks. To overcome these limitations, this article presents a table-driven, blind steganographic technique specifically designed for DNA sequences stored in the cloud. Our proposed technique offers superior embedding capabilities, reduces DNA expansion, and minimizes security risks. The proposed method introduces two unique proposals: Employing machine learning techniques for secret encryption and utilizing bit-shuffling to enhance message protection, thereby increasing resistance against cyber-attacks. Furthermore, the method supports hiding various types of messages. Compared to existing methods, the suggested scheme achieves a lower stego expansion rate. Additionally, a newly introduced parity check method strengthens security by preventing modifications to the stego DNA, such as man-in-the-middle attacks, chosen stego attacks, and modification attacks. These innovations enhance the overall security capabilities of the system. Experimental results validate the effectiveness of the proposed method, demonstrating accurate and error-free reconstruction of the cover, healthcare data, and DNA sequence. The proposed technique outperforms competing methods across all performance metrics, establishing its superiority in securing health data in the cloud.

Lossless Image Steganography Based on Invertible Neural Networks.

Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction.

A Proposal to Improve the Bit Plane Steganography based on the Complexity Calculation Technique

The video steganography technique is being studied and applied a lot today because of its benefits. In particular, the video steganography technique using Bit-Plane Complexity Segmentation (BPCS) has increasingly proven its effectiveness compared to other methods. In this paper, based on the theoretical basis of the BPCS method, we propose a new method to improve the efficiency of the steganography process. Accordingly, our improvement proposal in this paper is improving the complexity formula of the bit planes. Our new formula not only has improved the steganographic thresholds in the bit planes to find more planes hiding secret information, but also has ensured the amount of information hidden in the video and their safety. The experimental results in the paper have not only demonstrated the effectiveness of our proposed method but also provided a new mechanism for digital image analysis in general and video steganography techniques in particular.

A Modulo Function-Based Robust Asymmetric Variable Data Hiding Using DCT

This work presents a new asymmetric data hiding technique that hides a variable number of secret message bits in the discrete cosine transform (DCT) coefficients of a cover image using a modular distance technique. Prior to data hiding, the proposed framework transforms a cover image from a spatial domain to various frequency coefficients using DCT. The DCT coefficients are arranged in two groups: one with low-frequency coefficient, and the other with the medium and high-frequency coefficients. The medium and higher frequency coefficients are processed for variable data hiding asymmetrically. The proposed technique hides variable sets of secret information bits in different coefficients. The variation in hidden secret information is maintained using a key developed based on the modulo of distance of a coefficient from the reference point. The same key is also used to retrieve the confidential information at the receiver ends. The results reveal that the presented framework does not create any visually significant distortion, and thus the hidden information does not attract the human visual system (HVS). The technique also results in high data hiding efficiency.

Image Information Hiding Method Based on Image Compression and Deep Neural Network

Image steganography is a technique that hides secret information into the cover image to protect information security. The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image, which will cause the size of the secret image to be much smaller than the cover image. Therefore, the problem of small steganographic capacity needs to be solved urgently. This paper proposes a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression of the secret image. The compressed and reconstructed image is visually indistinguishable from the original image and facilitates more embedded data information later. Finally, the compressed image is transmitted to a SegNet deep neural network that contains a set of encoders and decoders to achieve image hiding and extraction. Experimental results show that the steganographic framework guarantees the quality of steganography while its relative steganographic capacity reaches 1. Besides, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values can reach 42 dB and 0.94, respectively.

A QR Code Secret Hiding Scheme against Contrast Analysis Attack for the Internet of Things

Due to the advantages of larger content and error correction capability, quick response (QR) code is commonly used as a tagging technology for the Internet of Things (IoT) recently. However, the cover message of QR code can be easily decoded by a QR code reader, which causes the security and privacy of the cover message to raise the important issues. In this paper we present a new secret hiding scheme based on QR code. The proposed scheme has low computational complexity and is suitable for low-power devices in IoT systems because of utilizing the error correction property of QR code to hide secret information. The proposed scheme hides the secret information without changing the cover message of QR code and the user can get the cover message by using a general scanner, which contributes to reducing attacker’s curiosity. The hidden secret information can be read by a special scanner with the help of the user key. One thing which is better than other known schemes is that the proposed scheme can resist contrast analysis attack. In addition, experimental results show the proposed scheme has feasibility, low computational complexity, and high hiding payload.

A review of forensic approaches to digital image Steganalysis

Traditional or Binary Steganalysis brands a digital object such as an image as stego or innocent only but modern day information security requires deeper insight about the embedded message. Forensic Steganalysis is the systemic application of different research techniques to gather further in-depth knowledge about the hidden secret information. Once an image is categorised as being stego, additional investigations are carried out to find the steganographic algorithm used to insert the covert message, estimate the length of such message and finally the stego key used to embed the message in various pixels of the stego image. A lot more literature is available on the review of the traditional steganalysis techniques as compared to forensic steganalysis. The present paper gives details about important forensic techniques as available in the steganalysis literature. The techniques presented and discussed relate to digital image domain for determination of the embedding algorithm, estimation of the secret message payload and stego key determination. The paper describes and compares different features of these forensic techniques. Discussions about significant performance metrics and evaluation parameters used in all phases further elaborate the comparative perspective. We identify potential challenges and explore areas of future work to boost the capabilities of present forensic steganalyzers.

New Steganography System Based on Huffman Coding and Fibonacci Decomposition

Hiding secret information in the image is a challenging and painstaking task in computer security and steganography system. Certainly, the absolute intricacy of attacks to security system makes it more attractive.in this research on steganography system involving information hiding,Huffman codding used to compress the secret code before embedding which provide high capacity and some security. Fibonacci decomposition used to represent the pixels in the cover image, which increase the robustness of the system. One byte used for mapping all the pixels properties. This makes the PSNR of the system higher due to random distribution of embedded bits. Finally, three kinds of evaluation are applied such as PSNR, chi-square attack, and HVS attack.

Blind decoding of image steganography using entropy model

Image steganography hides secret information in the cover images so naturally that the existence of hidden data in the stego-image is not recognisable. This Letter proposes a new approach to blind decoding of image steganography using the local entropy distributions of decoded images. The local entropy distributions of incorrectly decoded images are different from those of normal ones because of the abnormal image structures in the erroneously decoded images. This blind decoding in the image steganography is very useful to extract hidden image information because there are enormous least significant bit (LSB)-based steganography methods, and it is very hard to find the methods by observing manipulated LSBs.

A turtle shell-based visual secret sharing scheme with reversibility and authentication

Visual secret sharing (VSS), also called visual cryptography, allows a secret image to be divided into several shares and the secret image can only be recovered through the collaboration of the shares. Due to high security and low computational complexity, VSS has been widely used in secure communications. In this paper, we propose the first turtle shell (TS)-based VSS scheme that shares the secret information into two meaningful shares. Firstly, a TS reference matrix is established. Then, secret information is hidden in a cover image to construct two meaningful shares with the help of TS reference matrix. The hidden secret information can be extracted without distortion if both shares are gathered and the cover image can be correctly restored by solving the location conflict problem. Moreover, an invalid share can easily be detected by the proposed scheme. Experimental results indicate that the proposed scheme provides excellent visual quality and is superior to state-of-the-art VSS schemes.

Improved Secure Data Transfer Using Video Steganographic Technique

Information security using data hiding in video provides high embedding capacity and security. Steganography is one of the oldest data protecting methodologies deals with the embedding of data. Video Steganography hides secret information file within a video. Present day communications are treated to be “un-trusted” in terms of security, i.e. they are relatively easy to be hacked. The proposed technique is invented to hide secret information into a video file keeping two considerations in mind which are size and security of the cover video file. At the sender side, the secret information which is to be hidden is encoded into cover video file. Double layered security for the secret data can be achieved by encrypting confidential information and by embedding confidential information into cover video file frames using encrypted embedding technique.

Text Steganography using Extensions Kashida based on the Moon and Sun Letters Concept

Existing steganography methods are still lacking in terms of capacity. Hence, a new steganography method for Arabic text is proposed. The method hides secret information bits within Arabic letters using two features, which are the moon and sun letters and the redundant Arabic extension character “-” known as Kashida. The Arabic alphabet contains 28 letters, which are classified into 14 sun letters and 14 moon letters. This classification is based on the way these letters affect the pronunciation of the definite article (ال) at the beginning of words. This method uses the sun letters with one extension to hold the secret bits ‘01’, the sun letters with two extensions to hold the secret bits ‘10’, the moon letters with one extension to hold the secret bits ‘00’ and the moon letters with two extensions to hold the secret bits ‘11’. The capacity performance of the proposed method is then compared to three popular text steganographic methods. Capacity is measured based on two factors which are Embedding Ratio (ER) and The Efficiency Ratio (TER). The results show that the Letter Points and Extensions Method produces 24.91% and 21.56% as the average embedding ratio and the average efficiency ratio correspondingly. For the Two Extensions ‘Kashida’ Character Method, the results for the average embedding ratio and the efficiency ratio are 56.76% and 41.81%. For the Text Using Kashida Variation Algorithm method, the average embedding ratio and the average efficiency ratio are 31.61% and 27.82% respectively. Meanwhile, the average embedding ratio and the efficiency ratio for the Proposed Method are 61.16% and 55.70%. Hence, it is concluded that the Proposed Method outweighs the other three methods in terms of their embedding ratio and efficiency ratio which leads to the conclusion that the Proposed Method could provide higher capacity than the other methods.

A Proposed Algorithm for Securing Signal- Encryption using Cryptography and Steganography

era of digital transmission, security of information is crucial. Steganography and cryptography helps in providing this much needed data confidentiality. Steganography hides secret information into a cover medium and cryptography converts data into an unrecognizable form. For more secure communication RSA algorithm is been introduced, which is based on asymmetric key cryptography.By using Steganography methods third parties cannot percept the existence of message embedded in the audio file. The properties of the audio file remain the same after hiding the secret message. In this Paper a new-fangled substitution method (Discussed in Proposed Method section) for Steganography has been used with RSA Cryptosystem.

Reversible data hiding with high payload based on referred frequency for VQ compressed codes index

In 2007, Chang et al. proposed a reversible data hiding scheme for secret communication that hides secret information in the compression codes of a cover image, but their scheme has low hiding capacity and introduces extra m bits to reverse the original vector quantization (VQ) index of the cover image after the secret data are extracted. Instead of introducing m bits (where the size of the codebook is m bits) and using only one-third of the VQ indices of the cover image to hide the secret bit, we propose using only one bit to distinguish between indices of two clusters (i.e., cluster2 and cluster3). Not only the indices in cluster1 but also those in cluster2 and cluster3 can hide the secret bits. Our proposed scheme reduces the number of extra bits and increases the hiding capacity. The experiment results clearly showed that our proposed scheme outperforms Chang et al.׳s hiding scheme.

Review of an Improved Audio Steganographic Technique over LSB through Random Based Approach

In this digital world, huge amount information exchange takes place due to enhanced facilities of networking. Therefore it is necessary to secure the information which we transmit. The need for secured communication introduces the concept of Steganography. Steganography, the word itself indicates that information within information; it is the best technique to hide the secret information by using cover objects. Secret information may be a text, image or an audio file. But as per secret information format there are different steganographic techniques are available. This paper proposes a method of audio steganographic system that provides a unique platform to hide the secret information in audio file though the information is in text, image or in an audio format. So there is no need to go for different techniques of steganography as per information format. Many steganographic methods follow the LSB insertion technique to hide the secret information. But there are many statistical techniques available to determine if a stego object has been subjected to LSB Embedding. The proposed system hides secret information in audio file through random based approach and provides security by using PKE algorithm. This paper focuses on combining the strengths of cryptography and steganography for secured communication.

Uncompressed Image Steganography using BPCS: Survey and Analysis

Steganography is the art and science of hide secret information in some carrier data without leaving any apparent evidence of data alternation. In the past, people use hidden tattoos, invisible ink or punching on papers to convey stenographic data. Now, information is first hide in digital image, text, video and audio. This paper discusses existing BPCS (Bit Plane Complexity Segmentation) steganography techniques and presences of some modification. BPCS technique makes use of the characteristics of the human visible system. BPCS scheme allows for large capacity of embedded secret data and is highly customized. This algorithm offers higher hiding capacity due to that it exploits the variance of complex regions in each bit plane. In contrast, the BPCS algorithm provided a much more effective method for obtaining a 50% capacity since visual attacks did not suffice for detection. Keywords: BPCS, Data security, Information hiding, Steganography, Stego image

Properties of Images in LSB Plane

Each pixel of an Image (assuming 24 bit BMP) is stored in the three 8 bits corresponding to three colors. Bit Plane Slicing is the technique of breaking image into 8 planes corresponding to 8 different bit positions. The bit plane image corresponding to the plane of the most significant bit (MSB) has the maximum contribution to the total image and forms the majority of the visually significant image data and planes corresponding to other lower bit positions contribute only the subtle details of the image. And the bit-plane image corresponding to the LSB of the RGB value has the minimum contribution of only 1 out of total of 255 Intensity level to the total image. In spite of this the LSB Plane of the image is not insignificant. In fact it is used in Image Steganography for hiding secret information in the images. Image based Steganography is a potent tool used by Terrorists and Criminal organizations for securely broadcasting, dead-dropping and communicating malicious secret messages over the internet by hiding them in the images. Our cyber space is full of such mala-fide images containing hidden secret information. The most difficult aspect of tracking such malicious images is the lack of efficient and fast algorithms for identifying and isolating them from the bulk of innocent images. Analysis of the pixels in the LSB Plane of the image is very effective technique of identification of Distributing Spatial Domain Steganographic Algorithms. In this paper the properties of Information Pixels pixel present in the LSB Plane of any stego image generated by Distributing Algorithms is analyzed in detail. These properties will be very useful in identification of malafide stego images lurking in our cyber space. All the images and tables used in this paper are generated using MATLAB ©Image Processing Tool Box.

Quantum Steganography Embedded Any Secret Text without Changing the Content of Cover Data

Steganography is a technique hiding secret information within innocent-looking information (e.g., text, audio, image, video, and so on). In this paper, we propose a quantum steganography protocol using plain text as innocent-looking information called cover data. Our steganograpy protocol has three features. First, we can use any plain text that is inde-pendent of any secret message sent between parties. When we make stego data, we do not need to change the content of plain text at all. Second, embedded messages are not included in opened information (innocent-looking messages), but are included as phases of the entangled states. Finally, in quantum states shared between parties in advance, i.e., as quantum keys used when the parties recover secret messages from stego data, neither innocent-looking information nor the information of any secret message is included.

Text Hiding in Mobile Phone Simple Message Service Using Fonts

Problem statement: Hiding secret information in an ordinary mobile phone Simple Message Service (SMS). Approach: In mobile phones, there are two default types of fonts, System and Proportional fonts, which have similar figures to human vision and cannot recognized by human eye. The suggested method hides the information (0,1) in cover SMS message by changing the fonts of each character by one of those two fonts (0 represented by System font and 1 represented by Proportional fonts). After embedding secret information in cover message, the Stego message will look like an ordinary message but each character draw in one of these similarity fonts. Finally, at extract site, it must analyze each character font to retrieve secret information. This study has been implemented by J2ME (Java 2 Micro Edition) programming language to works in mobile (cellular) phones. Results: By using two similarity fonts, we can hide one bit in one character of mobile phone message. Conclusion: Good steganography depends on the human behavior and way of thinking. The most successful hiding method is the uncommon one. Although the proposed method is simple but it is unthinkable one and needs knowledge and experience to be discovered.