Biblio

The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Secrete message protection has become a focal point of the network security domain due to the problems of violating the network use policies and unauthorized access of the public network. These problems have led to data protection techniques such as cryptography, and steganography. Cryptography consists of encrypting secrete message to a ciphertext format and steganography consists of concealing the secrete message in codes that make up a digital file, such as an image, audio, and video. Steganography, which is different from cryptography, ensures hiding a secret message for secure transmission over the public network. This paper presents a steganographic approach using digital images for data hiding that aims to providing higher performance by combining fuzzy logic type I to pre-process the cover image and difference expansion techniques. The previous methods have used the original cover image to embed the secrete message. This paper provides a new method that first identifies the edges of a cover image and then proceeds with a difference expansion to embed the secrete message. The experimental results of this work identified an improvement of 10% of the existing method based on increased payload capacity and the visibility of the stego image.

The use of image data in multimedia communication based applications like military applications and medical images security applications are increasing every day and the secrecy of the image data is extremely important for such applications. A number of methods and techniques for securely transmitting images are proposed in the literature based on image encryption and steganography approaches. A novel mechanism for transmitting color images securely is proposed in this paper mainly based on public key cryptography mechanism also by combining the advantage of simplicity of symmetric schemes. The technique combines the strengths of Elliptic Curve Cryptography and the classical symmetric cryptographic mechanism called Hill Cipher encryption method. The technique also includes the concept of Magic Square for jumbling the pixels yielding maximum diffusion in the image pixels. In the performance evaluation, the proposed method proved that the new system works pretty well. The method is proved to be effective in maintaining the confidentiality of the image in transit and also for resisting security attacks.

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.

The importance of data and its transmission rate are increasing as the world is moving towards online services every day. Thus, providing data security is becoming of utmost importance. This paper proposes a secure data encryption and hiding method based on DNA cryptography and steganography. Our approach uses DNA for encryption and data hiding processes due to its high capacity and simplicity in securing various kinds of data. Our proposed method has two phases. In the first phase, it encrypts the data using DNA bases along with Huffman coding. In the second phase, it hides the encrypted data into a DNA sequence using a substitution algorithm. Our proposed method is blind and preserves biological functionality. The result shows a decent cracking probability with comparatively better capacity. Our proposed method has eliminated most limitations identified in the related works. Our proposed hybrid technique can provide a double layer of security to sensitive data.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The technique of concealing a confidential information in a carrier information is known as steganography. When we use digital images as carriers, it is termed as image steganography. The advancements in digital technology and the need for information security have given great significance for image steganographic methods in the area of secured communication. An efficient steganographic system is characterized by a good trade-off between its features such as imperceptibility and capacity. The proposed scheme implements an edge-detection based adaptive steganography with transform domain embedding, offering high imperceptibility and capacity. The scheme employs an adaptive embedding technique to select optimal data-hiding regions in carrier image, using Canny edge detection and a Convolutional Neural Network (CNN). Then, the secret image is embedded in the Dual-Tree Complex Wavelet Transform (DTCWT) coefficients of the selected carrier image blocks, with the help of Singular Value Decomposition (SVD). The analysis of the scheme is performed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC).

Image steganography is the operation of hiding a message into a cover image. the message can be text, codes, or image. Hiding an image into another is the proposed approach in this paper. Based on LSB coding, a k-LSB-based method is proposed using k least bits to hide the image. For decoding the hidden image, a region detection operation is used to know the blocks contains the hidden image. The resolution of stego image can be affected, for that, an image quality enhancement method is used to enhance the image resolution. To demonstrate the effectiveness of the proposed approach, we compare it with some of the state-of-the-art methods.

Text steganography aims to embed hidden messages in text information while the goal of text steganalysis is to identify the existence of hidden information or further uncover the embedded message from the text. Steganalysis has received significant attention recently for the security and privacy purpose. In this paper, we develop unsupervised learning approaches for text steganalysis. In particular, two detection models based on deep learning have been proposed to detect hidden information that may be embedded in text from a global and a local perspective. Extensive studies have been carried out on the Chinese poetry text steganography datasets. It is seen that the proposed models show strong empirical performance in steganographic text detection.

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.