Biblio

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.

Digital signal processor (DSP) intellectual property (IP) cores are the underlying hardware responsible for high performance data intensive applications. However an unauthorized IP vendor may counterfeit the DSP IPs and infuse them into the design-chain. Thus fake IPs or integrated circuits (ICs) are unknowingly integrated into consumer electronics (CE) systems, leading to reliability and safety issues for users. The latent solution to this threat is hardware steganography wherein vendor's secret information is covertly inserted into the design to enable detection of counterfeiting. A key-regulated hash-modules chaining based IP steganography is presented in our paper to secure against counterfeiting threat. The proposed approach yielded a robust steganography achieving very high security with regard to stego-key length than previous approaches.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

Although the Pixel-Value Differencing (PVD) steganography can avoid being detected by the RS steganalysis, the histogram of the pixel-value differences poses an abnormal distribution. Based on this hiding characteristic, this paper proposes a PVD steganalysis based on chi-Square statistics. The degrees of freedom were adopted to be tested for obtaining various detection accuracies (ACs). Experimental results demonstrate the detection accuracies are all above 80%. When the degrees of freedom are set as 10 while the accuracy is the best (AC = 83%). It means that the proposed Chi-Square based method is an efficient detection for PVD steganography.

Many portable imaging devices use the operation of "trunc" (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source. Steganalysis detectors need to be retrained since a steganalyst unaware of the existence of the trunc quantizer will experience 100% false alarm.

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

Information sharing through internet has becoming challenge due to high-risk factor of attacks to the information being transferred. In this paper, a novel image-encryption edge based Image steganography technique is proposed. The proposed algorithm uses logistic map for encrypting the information prior to transmission. Laplacian of Gaussian (LoG) edge operator is used to find edge areas of the colored-cover-image. Simulation analysis demonstrates that the proposed algorithm has a good amount of payload along with better results of security analysis. The proposed scheme is compared with the existing-methods.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.

The recent focus of cyber security on automated detection of malware for Android apps has omitted the study of some apps used for “legitimate” purposes, such as steganography apps. Mobile steganography apps can be used for delivering harmful messages, and while current research on steganalysis targets the detection of stego images using academic algorithms and well-built benchmarking image data sets, the community has overlooked uncovering a mobile app itself for its ability to perform steganographic embedding. Developing automatic tools for identifying the code in a suspect app as a stego app can be very challenging: steganography algorithms can be represented in a variety of ways, and there exists many image editing algorithms which appear similar to steganography algorithms.This paper proposes the first automated approach to detect Android steganography apps. We use symbolic execution to summarize an app’s image operation behavior into expression trees, and match the extracted expression trees with reference trees that represents the expected behavior of a steganography embedding process. We use a structural feature based similarity measure to calculate the similarity between expression trees. Our experiments show that, the propose approach can detect real world Android stego apps that implement common spatial domain and frequency domain embedding algorithms with a high degree of accuracy. Furthermore, our procedure describes a general framework that has the potential to be applied to other similar questions when studying program behaviors.

This paper proposes 2 multiple layer message security schemes. Information security is carried out through the implementation of cryptography, steganography and image processing techniques. In both schemes, the sensitive data is first encrypted by employing a chaotic function. In the first proposed scheme, LSB steganography is then applied to 2D slices of a 3D image. In the second proposed scheme, a corner detection filter is first applied to the 2D slices of a 3D image, then LSB embedding is carried out in those corner-detected pixels. The number of neighboring pixels used for corner detection is varied and its effect is noted. Performance of the proposed schemes is numerically evaluated using a number of metrics, including the mean squared error (MSE), the peak signal to noise ratio (PSNR), the structure similarity index measure (SSIM), the normalized cross-correlation (NCC), the image fidelity (IF), as well as the image difference (ID). The proposed schemes exhibit superior payload capacity and security in comparison to their counterparts from the literature.

Existing search-based coverless text steganography algorithms according to the characteristics of the text, do not need to modify the carrier, and have good resistance to detection, but they rely on a large text data set and have a limited hiding capacity. For this reason, this paper proposes a coverless steganography method based on the source XML file organization of the OOXML documents from a new perspective. It analyzes the organization of OOXML documents, and uses the differences of organization to construct the mapping between documents and secret information, so as to realize the coverless information hiding. To achieve the efficiency of information hiding, a compound tree model is designed and introduced to construct the OOXML document category library. Compared with the existing coverless information hiding methods, the text set size that this method relies on is significantly reduced, and the flexibility of the mapping is higher under the similar hiding capacity.

Steganography security is the main concern in today’s informative world. The fact is that communication takes place to hide information secretly. Steganography is the technique of hiding secret data within an ordinary, non-secret, file, text message and images. This technique avoids detection of the secret data then extracted at its destination. The main reason for using steganography is, we can hide any secret message behind its ordinary file. This work presents a unique technique for image steganography based on a 512-bit algorithm. The secure stego image is a very challenging task to give protection. Therefore we used the least significant bit (LSB) techniques for implementing stego and cover image. However, data encryption and decryption are used to embedded text and replace data into the least significant bit (LSB) for better approaches. Android-based interface used in encryption-decryption techniques that evaluated in this process.Contribution—this research work with 512-bit data simultaneously in a block cipher to reduce the time complexity of a system, android platform used for data encryption decryption process. Steganography model works with stego image that interacts with LSB techniques for data hiding.