Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

استرجاع الصور المستند إلى المحتوى (CBIR) هو تقنية تستخدم لاسترداد الصور من قاعدة بيانات الصور. ومع ذلك، فإن عملية CBIR تعاني من دقة أقل في استرداد الصور من قاعدة بيانات صور واسعة النطاق وضمان خصوصية الصور. تهدف هذه الورقة إلى معالجة قضايا الدقة باستخدام تقنيات التعلم العميق كطريقة CNN. أيضًا، توفير الخصوصية اللازمة للصور باستخدام طرق تشفير متماثلة تمامًا بواسطة Cheon و Kim و Kim و Song (CKKS). ولتحقيق هذه الأهداف تم اقتراح نظام RCNN_CKKS يتضمن جزأين. يستخرج الجزء الأول (المعالجة دون اتصال بالإنترنت–) لاستخراج الخصائص العالية المستوى استنادًا إلى طبقة التسطيح في شبكة عصبية تلافيفية (CNN) ثم يخزن هذه الميزات في مجموعة بيانات جديدة. في الجزء الثاني (المعالجة عبر الإنترنت) ، يرسل العميل الصورة المشفرة إلى الخادم ، والتي تعتمد على نموذج CNN المدرب لاستخراج ميزات الصورة المرسلة. بعد ذلك، تتم مقارنة الميزات المستخرجة مع الميزات المخزنة باستخدام طريقة Hamming Distance لاسترداد جميع الصور المتشابهة. أخيرًا، يقوم الخادم بتشفير جميع الصور المسترجعة وإرسالها إلى العميل. كانت نتائج التعلم العميق على الصور العادية 97.94٪ للتصنيف و98.94٪ للصور المسترجعة. في الوقت نفسه، تم استخدام اختبار NIST للتحقق من أمان CKKS عند تطبيقه على مجموعة بيانات المعهد الكندي للأبحاث المتقدمة (CIFAR-10). من خلال هذه النتائج، استنتج الباحثون أن التعلم العميق هو وسيلة فعالة لاستعادة الصور وأن طريقة CKKS مناسبة لحماية خصوصية الصورة.A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection

Forecasting the Exchange Rates of the Iraqi Dinar against the US Dollar using the Time Series model (ARIMA)

Estimating the exchange rate is considered a key tool for economic planning and reaching economic stability. This study aims to reach the best model for predicting exchange rates of Iraqi Dinar against the U.S. dollar in the period (2008-2017). For this purpose the following methods have been adopted: - Time-series analysis using the Box – Jenkins approach.                 Forecasts obtained from the two models were compared using both mean of the absolute values of the errors (MAE) and the square root of the mean square error (RMSE). The ARIMA (1,1,1) model produced the best forecasts and it can be used as a reliable method of estimating the exchange rate of any foreign currency

Entropy analysis and image encryption application based on a new chaotic system crossing a cylinder

Designing chaotic systems with specific features is a hot topic in nonlinear dynamics. In this study, a novel chaotic system is presented with a unique feature of crossing inside and outside of a cylinder repeatedly. This new system is thoroughly analyzed by the help of the bifurcation diagram, Lyapunov exponents' spectrum, and entropy measurement. Bifurcation analysis of the proposed system with two initiation methods reveals its multistability. As an engineering application, the system's efficiency is tested in image encryption. The complexity of the chaotic attractor of the proposed system makes it a proper choice for encryption. States of the chaotic attractor are used to shue the rows and columns of the image, and then the shued image is XORed with the states of chaotic attractor. The unpredictability of the chaotic attractor makes the encryption method very safe. The performance of the encryption method is analyzed using the histogram, correlation coefficient, Shannon entropy, and encryption quality. The results show that the encryption method using the proposed chaotic system has reliable performance. - 2019 by the authors.Scopu

Finding the Relevance Degree between an English Text and its Title

Keywords are useful tools as they give the shorter summary of the document. Keywords are useful for a variety of purposes including summarizing, indexing, labeling, categorization, clustering, and searching, and in this paper we will use keywords in order to find the relevance degree between an English text and its title. The proposed system solves this problem through simple statistic (Term frequency) and linguistic approaches by extracting the keywords of the title and keywords of the text (with their frequency that appear in the text) and finding the average of title's keywords frequency across the text that represent the relevance degree that required, with depending on a lexicon of a particular field(in this work we choose computer science field). This lexicon is represented using two different B+ trees one for non-keywords and the other for candidate keywords, these keywords was stored in a manner that prevent redundancy of these terms or even sub-terms to provide efficient memory usage and to minimize the search time. The proposed system was implemented using Visual Prolog 5.1 and after testing, it proved to be valuable for finding the degree of relevance between a text and its title (from point of view of accuracy and search time)

Combined Chebyshev and logistic maps to generate pseudorandom number generator for internet of things

Sensitive data exchanging among things over the Internet must be protected by a powerful cryptographic system. Conventional cryptographic such as advanced encryption standard (AES), and respiratory sinus arrhythmia (RSA) are not effective enough to protect internet of things (IoT) because of certain inveterate IoT properties like limited memory, computation, and bandwidth. Nowadays, chaotic maps with high sensitivity to initial conditions, strong ergodicity, and non-periodicity have been widely used in IoT security applications. So, it is suitable for IoT. Also, in a stream cipher method, the user needs to deliver the keystream to all clients in advance. Consequently, this paper proposed a method to solve the keys distribution problem based on combine both Chebyshev and logistic maps techniques as well as a master key to generate a random key. The suggested method was compared with the other stream cipher algorithms (Chacha20, RC4, Salsa20) by utilizing the same plaintext and master key as input parameters and the results were successful in the statistical national institute of standards and technology (NIST) test. Simultaneously, the suggestion was evaluated through different evaluation methods like statistical NIST test, histogram, Shannon entropy, correlation coefficient analysis, keyspace and key sensitivity, and others. All mentioned tests are passed successfully. Therefore, the suggested approach was proved it is effective in security issues

Image Steganography Based on Chaos Function and Randomize Function

The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital media transferred via the Internet including everything, whether texts, images or videos and audio, or animation. These media need high-security protection and high speed during its transmission from one site to another. In this study, a new method is suggested for hiding a gray-level image within a larger color image based on the proposed steganography map that merged chaotic function and randomize function. The size of the chaos and randomize functions is 16 bytes. Experimental results obtained a successful method based on mean squared error, signal-to-noise ratio, peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidly hide and extract ciphertext in and from the gray image. The original image and the stego image are difficult to distinguish because the correlation between them is very close to 1, indicating that attackers cannot easily differentiate these images with the naked eye. This condition can successfully hide information on the Internet

The Steganography Based On Chaotic System for Random LSB Positions

The objective of hiding text in an image is hiding text without raising suspicions that the image contains a hidden message or text, which leads to protecting and maintaining text confidentiality. The previous hiding methods have problems in capacity, randomization, and imperceptibility. This paper will be solved some of these problems; we suggested a new method for hiding text in an image. Firstly, encrypting the text by the AES-192 bit algorithm for obtaining a secret message. When the initial key of the AES-192 (bit) algorithm is generated by a chaotic system for randomness purposes, secondly, hiding the secret message is into a gray image for obtaining a stego-image. The hiding step is based on a proposed map that chooses from the last round of key expansion in the AES-192 algorithm. This map represented random positions of LSB in each byte of the gray image. The experimental result of this method proved a successful method based on metric criteria. Also, this method is the very speed for hiding ciphertext in the gray image as well as extracting ciphertext from the gray image. Also, it is very safe because it is difficult for attackers to distinguish between the original image and the stego image therefore the correlation between the original image and the stego- image is very close to 1

Block Cipher Nonlinear Confusion Components Based on New 5-D Hyperchaotic System

The security strengths of block ciphers greatly rely on the confusion components which have the tendency to transform the data nonlinearly into the perplexed form. This paper proposes to put forward a novel scheme of generating cryptographically strong nonlinear confusion components of block ciphers, usually termed as substitution-boxes (S-boxes). The anticipated S-box design scheme is based on a novel five-dimensional (5-D) chaotic system analyzed in this paper. The proposed 5-D dynamical system consists of hyperchaotic phenomenon, KY dimension, conservativity, unstable equilibrium point, and complex phase attractors which are suited for cryptographic applications. The S-box based on hyperchaotic system is made to evolve in order to generate an optimized S-box for high nonlinearity score to make it robust against many linear attacks. The performance analysis of proposed S-box demonstrates that it has bijectivity, high nonlinearity; satisfied strict avalanche criterion and bits independent criterion; low differential and linear probabilities. Moreover, performance appraisal of proposed S-box justifies its better strength and features over many recently investigated S-boxes

Image Encryption Based on Local Fractional Derivative Complex Logistic Map

Local fractional calculus (fractal calculus) plays a crucial role in applications, especially in computer sciences and engineering. One of these applications appears in the theory of chaos. Therefore, this paper studies the dynamics of a fractal complex logistic map and then employs this map to generate chaotic sequences for a new symmetric image encryption algorithm. Firstly, we derive the fractional complex logistic map and investigate its dynamics by determining its equilibria, geometric properties, and chaotic behavior. Secondly, the fractional chaotic sequences of the proposed map are employed to scramble and alter image pixels to increase resistance to decryption attacks. The output findings indicate that the proposed algorithm based on fractional complex logistic maps could effectively encrypt various kinds of images. Furthermore, it has better security performance than several existing algorithms