Research on image steganography analysis based on deep learning
تحقیق در مورد تجزیه و تحلیل استگانوگرافی تصویر بر اساس یادگیری عمیق-2019
Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges. Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the representative deep learning method CNN, the model is designed and adjusted according to the characteristics of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics such as neighborhood correlation. 2. A steganalysis feature learning method based on global information constraints is proposed. Based on the previous research of steganalysis method based on CNN, this work focuses on the importance of global information in steganalysis feature expression. 3. A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct steganalysis detectors without distinguishing specific types of steganalysis algorithms
Keywords: Steganalysis | Steganography | Feature learning | Deep learning | Convolutional neural network | Transfer learning | Multitask learning
Approaches for preserving content integrity of sensitive online Arabic content: A survey and research challenges
رویکردهای حفظ تمامیت محتوا از محتوای حساس آنلاین عربی: بررسی و چالش های تحقیقاتی-2019
Trends in Internet usage and accessing online content in different languages and formats are proliferating at a considerable speed. There is a vast amount of digital online content available in different formats that are sensitive in nature with respect to writing styles and arrangement of diacritics. However, research done in the area aimed at identifying the necessary techniques suitable for preserving content integrity of sensitive digital online content is limited. So, it is a challenge to determine the techniques most suitable for dif- ferent formats such as image or binary. Hence, preserving and verifying sensitive content constitutes an emerging problem and calls for timely solutions. The digital Holy Qur’an in Arabic, constitutes, one case of such sensitive content. Due to the different characteristics of the Arabic letters like diacritics (punctuation symbols), kashidas (extended letters) and other symbols, it is very easy to alter the original meaning of the text by simply chang- ing the arrangement of diacritics. This article surveys the different approaches that are presently employed in the process of preserving and verifying the content integrity of sen- sitive online content. We present the state-of-the-art in content integrity verification and address the existing challenges in preserving the integrity of sensitive texts using the Dig- ital Qur’an as a case study. The proposed taxonomy provides an effective classification and analysis of existing related schemes and their limitations. The paper discusses the recom- mendations of the expected efficiency of such approaches when applied for use in digital content integrity. Some of the main findings suggest unified approaches of watermarking and string matching approaches can be used to preserve content integrity of any sensitive digital content.
Keywords: Online sensitive content | Quran | Integrity | Watermarking | Cryptography | Steganography
Dynamic embedding strategy of VQ-based information hiding approach
استراتژی توکاری پویا از روش پنهان اطلاعات مبتنی بر VQ-2019
Information security is one of the most challenging issues. Cryptography and Steganography techniques are two popular methods for protecting data privacy. In this study, an information hiding method with dynamic embedding capacity based on vector quantization is proposed for protecting confidential data. To improve embedding capacity and image quality at the same time, dynamic-length secret bits are embedded into each pixel. Compared with previous approaches, the proposed method preforms better regarding the embedding capacity and image quality.
Keywords: Vector quantization | Dynamic embedding algorithm | Steganography
Image steganography using exploiting modification direction for compressed encrypted data
استگانوگرافی تصویر با استفاده از جهت اصلاح برای داده های رمزگذاری شده فشرده شده-2019
Building a balanced relation between image quality and the payload, the robustness of the method in facing electronic attacks and securing data, all the mentioned processes represent the main challenge in steganography. Here, a novel approach to steganography is suggested using Vigenere Cipher and Huffman Coding methods to encrypt and compress the mystery message content. This approach will raise the security and ensure the message content cannot be extracted without earlier knowledge of decrypting rules and the Huffman Dictionary Table. Later, the image is segmented into blocks, size (w*h) groups for each block and with each group having n pixels. Subsequently, the knight tour algorithm and arbitrary function are utilized to select which blocks and groups can be used to conceal the mystery digit within a specific pixel in the group randomly. This is to address the weakness of the Exploiting Modification Direction (EMD) technique that uses a serial selection to enhance the robustness of the suggested scheme. The EMD technique is then utilized to insert the mystery digits inside a specific pixel. Later, the chi-square method is employed to apply statistical attacks on the stego-image to estimate the suggested scheme robustness. The empirical outcomes show that the suggested scheme is more efficient compared to the old Steganography schemes with respect to Imperceptibility by PSNR of 55.71 dB, the Payload of 52,400 bytes and the robustness.
Keywords: Steganography | Cryptography | Exploiting modification direction | Knight tour | Vigenere cipher | Huffman coding
Improved Cohort Intelligence: A high capacity, swift and secure approach on JPEG image steganography
بهبود هوش گروهی: رویکردی با ظرفیت بالا ، سریع و ایمن در استگانوگرافی تصویر JPEG -2019
In the recent high level of information security was attained by combining the concepts of cryptogra- phy, steganography along with the nature inspired optimization algorithms. However, in today’s world computational speed plays a vital role for the success of any scientific method. The optimization algo- rithms, such as cohort Intelligence with Cognitive Computing (CICC) and Modified-Multi Random Start Local Search (M-MRSLS) were already implemented and applied for JPEG image steganography for 8 ×8 as well as 16 ×16 quantization table, respectively. Although results were satisfactory in terms of image quality and capacity, the computational time was high for most of the test images. To overcome this challenge, the paper proposes modified version of cohort intelligence (CI) algorithm referred to as Im- proved Cohort Intelligence (CI). The Improved CI algorithm was considered as a cryptography technique and implemented to generate optimized cipher text. Improved CI algorithm was further employed for JPEG image steganography to propose a reversible data hiding scheme. Experimentation was done on grey scale image, of size 256 ×256; both for 8 ×8 and 16 ×16 quantization table. Results validation of the proposed work exhibited very encouraging improvements in the computational cost
Keywords: Information security | Cryptography | Steganography | JPEG compression | Grey scale image | Improved CI
A novel image steganography technique based on quantum substitution boxes
یک تکنیک جدید استگانوگرافی تصویر مبتنی بر جعبه های جایگزینی کوانتومی-2019
Substitution boxes play an essential role in designing secure cryptosystems. With the evolution of quantum technologies, current data security mechanisms may be broken due to their construction based on mathematical computation. Quantum walks, a universal quantum computational model, play an essential role in designing quantum algorithms. We utilize the benefits of quantum walks to present a novel technique for constructing substitution boxes (S-boxes) based on quantum walks (QWs). The performance of the presented QWs S-box technique is evaluated by S-box evaluation criteria, and our results prove that the constructed S-box has vital qualities for viable applications in security purposes. Furthermore, a new technique for image steganography is constructed. The proposed technique is an integrated mechanism between classical data hiding and quantum walks to achieve better security for the embedded data. The embedding and extraction procedures are controlled by QWs S-box. The inclusion of cryptographic QWs S-box ensures the security of both embedding and extraction phases. At the extraction phase, only the stego image and the secret values are needed for constructing the secret data. Experimental results demonstrate that the presented technique has a high-security, high embedding capacity and good visual quality.
Keywords:Data hiding | Image steganography | Quantum walks | Cryptography | Substitution boxes
Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key: A deep learning approach for telemedicine
Steganalysis با استفاده از یک شبکه عصبی کانولوشن با استفاده از فیلترهای کانولاسیون بزرگ برای جاسازی فرآیند با همان کلید استگو: روش یادگیری عمیق برای پزشکی از راه دور -2017
Steganography, the art to hide information inside host media like pictures and movies, and steganalysis, its countermeasure attempting to detect the presence of an hidden information within an innocent-looking document, are frequently reported as promising information security techniques for telemedicine. For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovative classification procedures, can be deduced by a deep learning approach called convolutional neural networks (CNN). This kind of deep learning networks is so well-suited for classification tasks based on the detection of variations in 2D shapes that it is the state-of-the-art in many image recognition problems.
KEYWORDS : Telemedicine | security | Steganography | Steganalyzis | Deep learning
یک مدل پنهان نگاری حاصل از ترکیب روش جایگزینی LSB و روش PVD
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 15
اخیرا تکنیکهای پنهان نگاری (استگانوگرافی) از طریق ترکیب جایگزینی LSB و اختلاف مقادیر پیکسلها (PVD) مطرح شده اند تا ظرفیت پنهان سازی و نسبت سیگنال به نویز (PSNR) را افزایش دهند. در این مقاله یک تکنیک پنهان نگاری (استگانوگرافی) را با استفاده از جایگزینی LSB و PVD معرفی می کنیم. تصویر به بلوکهای 2*2 در مد غیر همپوشان تقسیم می شود. برای هر بلوک پیکسل 2*2 ، پیکسل بالا سمت چپ با k بیت دیتا و با استفاده از جایگزینی LSB پنهان نگاری می شود. سپس مقدار جدید این پیکسل جهت محاسبه ی اختلاف مقادیر پیکسلها (PVD) ی، بالا سمت راست، پایین سمت چپ، و پایین سمت راست در بلوک، مورد استفاده قرار می گیرد. سپس بیتهای داده با استفاده از این اختلاف مقادیر پیکسلهای سه جهته پنهان نگاری می شوند. هر دو لبه ی افقی و عمودی در نظر گرفته شده اند. با استفاده از جدول رنج جداگانه دو نوع (Type) پیشنهاد شده است. در نوع اول (Type 1) معیار PSNR بهبود یافته و در نوع دوم (Type 2) هم معیار PSNR و هم ظرفیت پنهان سازی بهبود یافته است.
کلمات کلیدی: پنهان کردن اطلاعات | کمترین تفاضل بیت معنی دار | تفاضل مقدار پیکسل | پنهان نگاری
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