A new approach for enhancing LSB steganography using bidirectional coding scheme

This paper proposes a new algorithm for embedding private information within a cover image. Unlike all other already existing algorithms, this one tends to employ the data of the carrier image more efficiently such that the image looks less distorted. As a consequence, the private data is maintained unperceived and the sent information stays unsuspicious.  This task is achieved by dividing the least significant bit plane of the cover image into fixed size blocks, and then embedding the required top-secret message within each block using one of two opposite ways depending on the extent of similarity of each block with the private information needed to be hidden. This technique will contribute to lessen the number of bits needed to be changed in the cover image to accommodate the private data, and hence will substantially reduce the   amount of distortion in the stego-image when compared to the classic LSB image steganography algorithms

Análisis forense digital y su papel en la promoción del enjuiciamiento penal

Digital forensics is essentially synonymous with computer forensics, but the term "digital forensics" is generally used for the technical review of all devices that have the ability to store data. Today, digital criminology is challenged in cloud computing. The first problem is to understand why and how criminal and social actions are so unique and complex. The second problem is the lack of accurate scientific tools for forensic medicine in cyberspace. So far, no complete tools or explanations for criminology have been provided in the virtual infrastructure, and no training for security researchers has been provided in detail. Therefore, the author of the present descriptive-analytical research is based on library resources and using fish taking tools. To investigate suspicious cases related to cyberspace, criminologists must be well-equipped with technical and legal issues to deal with. In this article, we analyze digital criminology and its role in judicial law. The benefit of computer forensic knowledge is not only an indispensable necessity for security and judicial institutions, but also professional users and owners of computer systems, systems and networks must be fully aware of and properly comply with its legal and technical requirements.El análisis forense digital es esencialmente sinónimo de análisis forense informático, pero el término "análisis forense digital" se utiliza generalmente para la revisión técnica de todos los dispositivos que tienen la capacidad de almacenar datos. Hoy en día, la criminología digital se enfrenta al desafío de la computación en la nube. El primer problema es comprender por qué y cómo las acciones criminales y sociales son tan únicas y complejas. El segundo problema es la falta de herramientas científicas precisas para la medicina forense en el ciberespacio. Hasta ahora, no se han proporcionado herramientas completas o explicaciones para la criminología en la infraestructura virtual, y no se ha proporcionado ninguna formación detallada a los investigadores de seguridad. Por lo tanto, el autor de la presente investigación descriptivo-analítica se basa en los recursos de la biblioteca y en el uso de herramientas de pesca. Para investigar casos sospechosos relacionados con el ciberespacio, los criminólogos deben estar bien equipados con los problemas técnicos y legales que abordar. En este artículo analizamos la criminología digital y su papel en el derecho judicial. El beneficio del conocimiento forense informático no solo es una necesidad indispensable para las instituciones de seguridad y judiciales, sino que también los usuarios profesionales y propietarios de sistemas, sistemas y redes informáticas deben conocer y cumplir debidamente sus requisitos legales y técnicos

Detecting browser drive-by exploits in images using deep learning

Steganography is the set of techniques aiming to hide information in messages as images. Recently, stenographic techniques have been combined with polyglot attacks to deliver exploits in Web browsers. Machine learning approaches have been proposed in previous works as a solution for detecting stenography in images, but the specifics of hiding exploit code have not been systematically addressed to date. This paper proposes the use of deep learning methods for such detection, accounting for the specifics of the situation in which the images and the malicious content are delivered using Spatial and Frequency Domain Steganography algorithms. The methods were evaluated by using benchmark image databases with collections of JavaScript exploits, for different density levels and steganographic techniques in images. A convolutional neural network was built to classify the infected images with a validation accuracy around 98.61% and a validation AUC score of 99.75%

An Efficient Light-weight LSB steganography with Deep learning Steganalysis

Active research is going on to securely transmit a secret message or so-called steganography by using data-hiding techniques in digital images. After assessing the state-of-the-art research work, we found, most of the existing solutions are not promising and are ineffective against machine learning-based steganalysis. In this paper, a lightweight steganography scheme is presented through graphical key embedding and obfuscation of data through encryption. By keeping a mindset of industrial applicability, to show the effectiveness of the proposed scheme, we emphasized mainly deep learning-based steganalysis. The proposed steganography algorithm containing two schemes withstands not only statistical pattern recognizers but also machine learning steganalysis through feature extraction using a well-known pre-trained deep learning network Xception. We provided a detailed protocol of the algorithm for different scenarios and implementation details. Furthermore, different performance metrics are also evaluated with statistical and machine learning performance analysis. The results were quite impressive with respect to the state of the arts. We received 2.55% accuracy through statistical steganalysis and machine learning steganalysis gave maximum of 49.93~50% correctly classified instances in good condition.Comment: Accepted pape

Image Steganography Using Web Application

Nowadays most people are workaholics, driven by intense peer pressure to break new ground. Therefore, rather than using books as their source material, photographic memories are kept as images. However, to protect sensitive information, reliable and secure communication techniques are necessary in many real-world situations. This research offers a user-friendly interface for securely embedding and extracting secret information within digital images. The primary goals are to design a steganographic algorithm for concealing data in images and to evaluate the usability of image steganography while keeping the quality of images and the access to decode the image. The application utilizes an advanced steganographic algorithm, which is Randomized Least Significant Bit (RLSB), to ensure robust data concealment while maintaining the visual integrity of the cover image. Users can upload their desired image, select the preferred steganographic algorithm, and encode the hidden data for added security. The web application supports encoding and decoding images for concealing data in images. To evaluate its performance, extensive testing was conducted, including embedding and extracting data using different image formats. The results demonstrated the application's effectiveness in hiding information while preserving image quality. The web application proved to be a versatile and practical tool with applications in various fields such as cryptography and digital forensics. In conclusion, the Image Steganography Web Application provides a convenient and secure solution for individuals and organizations needing to transmit sensitive data covertly within images, ensuring data privacy and integrity in an intuitive and user-friendly manner

Steganalisis Blind dengan Metode Convolutional Neural Network (CNN) Yedroudj- Net terhadap Tools Steganografi

Steganalisis digunakan untuk mendeteksi ada atau tidaknya file steganografi. Salah satu kategori steganalisis adalah blind steganalisis, yaitu cara untuk mendeteksi file rahasia tanpa mengetahui metode steganografi apa yang digunakan. Sebuah penelitian mengusulkan bahwa metode Convolutional Neural Networks (CNN) dapat mendeteksi file steganografi menggunakan metode terbaru dengan nilai probabilitas kesalahan rendah dibandingkan metode lain, yaitu CNN Yedroudj-net. Sebagai metode steganalisis Machine Learning terbaru, diperlukan eksperimen untuk mengetahui apakah Yedroudj-net dapat menjadi steganalisis untuk keluaran dari tools steganografi yang biasa digunakan. Mengetahui kinerja CNN Yedroudj-net sangat penting, untuk mengukur tingkat kemampuannya dalam hal steganalisis dari beberapa tools. Apalagi sejauh ini, kinerja Machine Learning masih diragukan dalam blind steganalisis. Ditambah beberapa penelitian sebelumnya hanya berfokus pada metode tertentu untuk membuktikan kinerja teknik yang diusulkan, termasuk Yedroudj-net. Penelitian ini akan menggunakan lima alat yang cukup baik dalam hal steganografi, yaitu Hide In Picture (HIP), OpenStego, SilentEye, Steg dan S-Tools, yang tidak diketahui secara pasti metode steganografi apa yang digunakan pada alat tersebut. Metode Yedroudj-net akan diimplementasikan dalam file steganografi dari output lima alat. Kemudian perbandingan dengan tools steganalisis lain, yaitu StegSpy. Hasil penelitian menunjukkan bahwa Yedroudj-net bisa mendeteksi keberadaan file steganografi. Namun, jika dibandingkan dengan StegSpy hasil gambar yang tidak terdeteksi lebih tinggi.AbstractSteganalysis is used to detect the presence or absence of steganograpy files. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. A study proposes that the Convolutional Neural Networks (CNN) method can detect steganographic files using the latest method with a low error probability value compared to other methods, namely CNN Yedroudj-net. As the latest Machine Learning steganalysis method, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of commonly used steganography tools. Knowing the performance of CNN Yedroudj-net is very important, to measure the level of ability in terms of steganalysis from several tools. Especially so far, Machine Learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This research will use five tools that are good enough in terms of steganography, namely Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which is not known exactly what steganography methods are used on the tool. The Yedroudj-net method will be implemented in a steganographic file from the output of five tools. Then compare with other steganalysis tools, namely StegSpy. The results showed that Yedroudj-net could detect the presence of steganographic files. However, when compared with StegSpy the results of undetected images are higher

Anti-Forensics with Steganographic File Embedding in Digital Image Using Genetic Algorithm

In this study, a steganography method on digital images as anti-forensics by utilizing genetic algorithms was proposed. Genetic Algorithms are artificial intelligence whose functions are optimization and search. The purpose of this research is to optimize steganography as anti-forensic by applying a Genetic Algorithm and combined with the Hilbert curve, lempel Ziv Markov chain, and least significant bit. The result provides a new steganography method by combining various existing methods. The proposed method will be tested for image quality using PSNR, SSIM, Chi-Squared steganalysis and RS-Analysis, and extraction test. The novelty obtained from the developed method is that the steganography method is as optimal as anti-forensic in keeping confidential data, has a large embedding capacity, and is able to be undetected using forensic methods. The results can maintain data confidentiality, have a large embedding capacity, and are able to be undetected using forensic methods. The proposed method got better performance rather than the previous method because PSNR and SSIM values are high, secret data can be received back as long as the pixel value doesn't change, and the size of the embedding capacity. The proposed method has more ability to embed various types of payload/ secret data because of the way it works, which splits byte files into binary. The proposed method also has the ability not to be detected when forensic image testing is carried out

An improved image steganography scheme based on distinction grade value and secret message encryption

Steganography is an emerging and greatly demanding technique for secure information communication over the internet using a secret cover object. It can be used for a wide range of applications such as safe circulation of secret data in intelligence, industry, health care, habitat, online voting, mobile banking and military. Commonly, digital images are used as covers for the steganography owing to their redundancy in the representation, making them hidden to the intruders, hackers, adversaries, unauthorized users. Still, any steganography system launched over the Internet can be cracked upon recognizing the stego cover. Thus, the undetectability that involves data imperceptibility or concealment and security is the significant trait of any steganography system. Presently, the design and development of an effective image steganography system are facing several challenges including low capacity, poor robustness and imperceptibility. To surmount such limitations, it is important to improve the capacity and security of the steganography system while maintaining a high signal-to-noise ratio (PSNR). Based on these factors, this study is aimed to design and develop a distinction grade value (DGV) method to effectively embed the secret data into a cover image for achieving a robust steganography scheme. The design and implementation of the proposed scheme involved three phases. First, a new encryption method called the shuffle the segments of secret message (SSSM) was incorporated with an enhanced Huffman compression algorithm to improve the text security and payload capacity of the scheme. Second, the Fibonacci-based image transformation decomposition method was used to extend the pixel's bit from 8 to 12 for improving the robustness of the scheme. Third, an improved embedding method was utilized by integrating a random block/pixel selection with the DGV and implicit secret key generation for enhancing the imperceptibility of the scheme. The performance of the proposed scheme was assessed experimentally to determine the imperceptibility, security, robustness and capacity. The standard USC-SIPI images dataset were used as the benchmarking for the performance evaluation and comparison of the proposed scheme with the previous works. The resistance of the proposed scheme was tested against the statistical, X2 , Histogram and non-structural steganalysis detection attacks. The obtained PSNR values revealed the accomplishment of higher imperceptibility and security by the proposed DGV scheme while a higher capacity compared to previous works. In short, the proposed steganography scheme outperformed the commercially available data hiding schemes, thereby resolved the existing issues

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications