Securing communication and information is known as cryptography. To convert messages from plain text to cipher text and the other way around. It is the process of protecting the data and sending it to the right audience so they can understand and process it. Hence, unauthorized access is avoided. This work suggests leveraging DNA technology for encrypt and decrypt the data. The main aim of utilizing the AES in this stage will transform ASCII code to hexadecimal to binary coded form and generate DNA. The message is encrypted with a random key. Shared key used for encrypt and decrypt the data. The encrypted data will be disguised as an image using steganography. To protect our data from hijackers, assailants, and muggers, it is frequently employed in institutions, banking, etc.

Cryptography and steganography is a method to secure private data. Those methods can also be combined for a more robust data security method. In this paper, we proposed a combination of cryptography and steganography methods that exploit some properties of Deoxyribonucleic Acid (DNA) for securing private data. The proposed work aims to enhance a POST-quantum DNA cryptography method by combining it with the steganography method. There are two parts to the proposed method; the first one is to encrypt private messages using a Kyber-DNA cryptography method. The second one is to embed encrypted private messages into a DNA sequence. The proposed method was then compared to another similar method RSA-DNA, El Gamal-DNA, and ECC-DNA. The proposed method is also compared with a similar POST-quantum method, that is NTRU-DNA. All those methods are compared in embedding speed test and extracting speed test. From those tests, it can be concluded that the proposed method has performance slightly lower than El Gamal-DNA and ECC-DNA but faster than RSA-DNA and NTRU-DNA

In this paper will be described a new security protocol for secret sharing and hiding, which use selected personal features. Such technique allows to create human-oriented personalized security protocols dedicated for particular users. Proposed method may be applied in dispersed computing systems, where secret data should be divided into particular number of parts.

The rate of development in today s IT industry is unprecedented. Information is being stored and transformed at a faster and faster rate. Therefore, the importance of protecting sensitive data is growing. Everyone is concerned about preventing hackers from gaining access to their personal data. Several algorithms from the fields of classical cryptography and steganography can be used to conceal data. DNA cryptography is an emerging field that aims to protect information kept in DNA. DNA cryptography takes use of DNA s innate computing power at the molecular level. In this paper, we are exploring diverse cryptographic algorithms and methods that are used to secure the stored data in DNA. The paper represents the critical review that includes merits and demerits of the diverse cryptographic algorithms for securing the data in DNA.

Multicore Computing Security - This paper deals with hash based secure chaotic steganography technique for hiding secret information, into the cover image. Hash function has been used in the proposed work for computing the Non LSB positions for hiding the secret data bits. Secret is encoded with chaotic sequences and randomness of the sequences has been validated with NIST test suite. Shared memory implementation for faster execution of the proposed security technique has been done in OpenMP platform. Sequential and the parallel versions of the techniques have been implemented in C++, OpenMP and simulated in the Intel Haswell processor based multi-core environment. With the advantages offered by multicore processors the proposed technique ensures low time complexity. Significant speedup and linear scalability have been reported with increase in the number of threads. Standard statistical validation test results viz. PSNR, Euclidean distance, histogram analysis, SSIM index applied to validate the quality of stego image show satisfactory results.

Data or information are being transferred at an enormous pace and hence protecting and securing this transmission of data are very important and have been very challenging. Cryptography and Steganography are the most broadly used techniques for safeguarding data by encryption of data and hiding the existence of data. A multi-layered secure transmission can be achieved by combining Cryptography with Steganography and by adding message authentication ensuring the confidentiality of the message. Different approach towards Steganography implementation is proposed using rotations and flips to prevent detection of encoded messages. Compression of multimedia files is set up for increasing the speed of encoding and consuming less storage space. The HMAC (Hash-based Authentication Code) algorithm is chosen for message authentication and integrity. The performance of the proposed Steganography methods is concluded using Histogram comparative analysis. Simulations have been performed to back the reliability of the proposed method.

In the present innovation, for the trading of information, the internet is the most well-known and significant medium. With the progression of the web and data innovation, computerized media has become perhaps the most famous and notable data transfer tools. This advanced information incorporates text, pictures, sound, video etc moved over the public organization. The majority of these advanced media appear as pictures and are a significant part in different applications, for example, chat, talk, news, website, web-based business, email, and digital books. The content is still facing various challenges in which including the issues of protection of copyright, modification, authentication. Cryptography, steganography, embedding techniques is widely used to secure the digital data. In this present the hybrid model of LSB steganography and Advanced Encryption Standard (AES) cryptography techniques to enhanced the security of the digital image and text that is undeniably challenging to break by the unapproved person. The security level of the secret information is estimated in the term of MSE and PSNR for better hiding required the low MSE and high PSNR values.

In our time the rapid growth of internet and digital communications has been required to be protected from illegal users. It is important to secure the information transmitted between the sender and receiver over the communication channels such as the internet, since it is a public environment. Cryptography and Steganography are the most popular techniques used for sending data in secrete way. In this paper, we are proposing a new algorithm that combines both cryptography and steganography in order to increase the level of data security against attackers. In cryptography, we are using affine hill cipher method; while in steganography we are using Hybrid edge detection with LSB to hide the message. Our paper shows how we can use image edges to hide text message. Grayscale images are used for our experiments and a comparison is developed based on using different edge detection operators such as (canny-LoG ) and (Canny-Sobel). Their performance is measured using PSNR (Peak Signal to Noise ratio), MSE (Mean Squared Error) and EC (Embedding Capacity). The results indicate that, using hybrid edge detection (canny- LoG) with LSB for hiding data could provide high embedding capacity than using hybrid edge detection (canny- Sobel) with LSB. We could prove that hiding in the image edge area could preserve the imperceptibility of the Stego-image. This paper has also proved that the secrete message was extracted successfully without any distortion.

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.

Minimizing embedding impact model of steganography has good performance for steganalysis detection. By using effective distortion cost function and coding method, steganography under this model becomes the mainstream embedding framework recently. In this paper, to improve the anti-detection performance, a new steganography optimization model by constructing a reference cover is proposed. First, a reference cover is construed by performing a filtering operation on the cover image. Then, by minimizing the residual between the reference cover and the original cover, the optimization function is formulated considering the effect of different modification directions. With correcting the distortion cost of +1 and \_1 modification operations, the stego image obtained by the proposed method is more consistent with the natural image. Finally, by applying the proposed framework to the cost function of the well-known HILL embedding, experimental results show that the anti-detection performance of the proposed method is better than the traditional method.

Steganography is the technique of hiding a confidential message in an ordinary message where the extraction of embedded information is done at its destination. Among the different carrier files formats; digital images are the most popular. This paper presents a Wavelet-based method for hiding secret information in digital images where skin areas are identified and used as a region of interest. The work presented here is an extension of a method published earlier by the authors that utilized a rule-based approach to detect skin regions. The proposed method, proposed embedding the secret data into the integer Wavelet coefficients of the approximation sub-band of the cover image. When compared to the original technique, experimental results showed a lower error percentage between skin maps detected before the embedding and during the extraction processes. This eventually increased the similarity between the original and the retrieved secret image.

With the development of social networks, traditional covert communication requires more consideration of lossy processes of Social Network Platforms (SNPs), which is called robust steganography. Since JPEG compression is a universal processing of SNPs, a method using repeated JPEG compression to fit transport channel matching is recently proposed and shows strong compression-resist performance. However, the repeated JPEG compression will inevitably introduce other artifacts into the stego image. Using only traditional steganalysis methods does not work well towards such robust steganography under low payload. In this paper, we propose a simple and effective method to detect the mentioned steganography by chasing both steganographic perturbations as well as continuous compression artifacts. We introduce compression-forensic features as a complement to steganalysis features, and then use the ensemble classifier for detection. Experiments demonstrate that this method owns a similar and better performance with respect to both traditional and neural-network-based steganalysis.

Edge detection based embedding techniques are famous for data security and image quality preservation. These techniques use diverse edge detectors to classify edge and non-edge pixels in an image and then implant secrets in one or both of these classes. Image with conceived data is called stego image. It is noticeable that none of such researches tries to reform the original image from the stego one. Rather, they devote their concentration to extract the hidden message only. This research presents a solution to the raised reversibility problem. Like the others, our research, first, applies an edge detector e.g., canny, in a cover image. The scheme next collects \$n\$-LSBs of each of edge pixels and finally, concatenates them with encrypted message stream. This method applies a lossless compression algorithm to that processed stream. Compression factor is taken such a way that the length of compressed stream does not exceed the length of collected LSBs. The compressed message stream is then implanted only in the edge pixels by \$n\$-LSB substitution method. As the scheme does not destroy the originality of non-edge pixels, it presents better stego quality. By incorporation the mechanisms of encryption, concatenation, compression and \$n\$-LSB, the method has enriched the security of implanted data. The research shows its effectiveness while implanting a small sized message.

Protection of private and sensitive information is the most alarming issue for security providers in surveillance videos. So to provide privacy as well as to enhance secrecy in surveillance video without affecting its efficiency in detection of violent activities is a challenging task. Here a steganography based algorithm has been proposed which hides private information inside the surveillance video without affecting its accuracy in criminal activity detection. Preprocessing of the surveillance video has been performed using Tunable Q-factor Wavelet Transform (TQWT), secret data has been hidden using Discrete Wavelet Transform (DWT) and after adding payload to the surveillance video, detection of criminal activities has been conducted with maintaining same accuracy as original surveillance video. UCF-crime dataset has been used to validate the proposed framework. Feature extraction is performed and after feature selection it has been trained to Temporal Convolutional Network (TCN) for detection. Performance measure has been compared to the state-of-the-art methods which shows that application of steganography does not affect the detection rate while preserving the perceptual quality of the surveillance video.

In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

Since data security is an important branch of the wide concept of security, using simple and interpretable data security methods is deemed necessary. A considerable volume of data that is transferred through the internet is in the form of image. Therefore, several methods have focused on encrypting and decrypting images but some of the conventional algorithms are complex and time consuming. On the other hand, denial method or steganography has attracted the researchers' attention leading to more security for transferring images. This is because attackers are not aware of encryption on images and therefore they do not try to decrypt them. Here, one of the most effective and simplest operators (XOR) is employed. The received shares in destination only with XOR operation can recover original images. Users are not necessary to be familiar with computer programing, data coding and the execution time is lesser compared to chaos-based methods or coding table. Nevertheless, for designing the key when we have messy images, we use chaotic functions. Here, in addition to use the XOR operation, eliminating the pixel expansion and meaningfulness of the shared images is of interest. This method is simple and efficient and use both encryption and steganography; therefore, it can guarantee the security of transferred images.