Improving random number generators by chaotic iterations. Application in data hiding

In this paper, a new pseudo-random number generator (PRNG) based on chaotic iterations is proposed. This method also combines the digits of two XORshifts PRNGs. The statistical properties of this new generator are improved: the generated sequences can pass all the DieHARD statistical test suite. In addition, this generator behaves chaotically, as defined by Devaney. This makes our generator suitable for cryptographic applications. An illustration in the field of data hiding is presented and the robustness of the obtained data hiding algorithm against attacks is evaluated.Comment: 6 pages, 8 figures, In ICCASM 2010, Int. Conf. on Computer Application and System Modeling, Taiyuan, China, pages ***--***, October 201

Hiding Moving Objects in Video Sequences

Maskování pohybujících se objektů ve videosekvenci je zajímavé téma v oblasti počítačového vidění. V této práci je navržena metoda pro sledování a maskování jednoho objektu v reálném čase. Předpokládá se vstupní video pořízené statickou kamerou. Detekce pohybu je založena na odečítání pozadí, které je reprezentováno směsí Gaussových funkcí. Sledování objektu je realizováno kombinací porovnávání tzv. blobů a výpočtu optického toku metodou Lucas-Kanade. Následné maskování využívá vytvořeného modelu pozadí.Hiding moving objects in video sequences is an interesting topic in computer vision. In this thesis, a real-time single object tracking and removal method is proposed. An input video is assumed to be captured with static video camera. Motion detection is based on a background subtraction using Mixture of Gaussians as the representation of background. The object tracking is realized by a combination of blobs comparison and computation of optical flow using Lucas-Kanade method. Consequent object removal makes use of created background model.

Information Hiding Based on DNA Sequences

يعد أمن المعلومات مصدر قلق رئيسي، خاصة في ضوء التوسع السريع في استخدام الإنترنت في السنوات الأخيرة. نتيجة لهذا التوسع، كانت هناك حالات وصول غير قانوني، والتي تم تخفيفها من خلال اعتماد مجموعة متنوعة من بروتوكولات الاتصال الآمن، بما في ذلك التشفير وإخفاء البيانات. في السنوات الأخيرة، كانت هناك زيادة في استخدام الحمض النووي للتشفير وإخفاء البيانات كناقل، مع الاستفادة من قدراته الجزيئية الحيوية. في إخفاء البيانات. نتيجة لذلك، في نهج إخفاء البيانات، يتم استخدام قواعد الحمض النووي كناقل للمعلومات لتعزيز الأمن. يندمج علم إخفاء المعلومات والتشفير المستند إلى الحمض النووي بين السمات البيولوجية والتقنيات التقليدية من أجل تحقيق خوارزمية مؤمنة جيدًا تستغلها. لذلك، توفر تسلسلات الحمض النووي قدرة عالية على البيانات بما في ذلك الحفاظ على الخصائص الكيميائية والبيولوجية لتسلسل الحمض النووي.Information security is a major source of worry, especially in light of the rapid expansion of internet use in recent years. As a result of this expansion, there have been incidences of illegal access, which have been mitigated by the adoption of a variety of secure communication protocols, including encryption and data concealment. DNA's bio-molecular properties have seen an uptick in popularity as a carrier for cryptography and data hiding in recent years. when information needs to be hidden. Therefore, DNA bases are utilized as information carriers in the data concealing strategy to increase safety. DNA-based steganography and cryptography combine a biological property with conventional methods to provide an algorithm with increased security. Because of their ability to maintain their chemical and biological characteristics, DNA sequences also have a high data capacity

Data hiding method based on D basic characteristics

The main target of this paper is to propose an algorithm to implement data hiding in DNA sequences to increase the complexity by using software point of view. By utilizing some intere sting features of DNA sequences, the implementation of a data hiding is applied. The algorithm which has been proposed here is based on binary coding and the complementary pair rules. Therefore, DNA reference sequence is chosen and also a secret message M is hidden into it. After applying three steps, M ´ ´ ´ is come out. Finally, M ´ ´ ´ is sent to the receiver. When the receiver takes the M ´ ´ ´, the process of identifying and extracting the original message M, which has been hidden in DNA reference sequence, begins. In addition, security issues are demonstrated to inspect the complexity of the algorithm

Multi-Carrier Steganographic Algorithm Using File Fragmentation of FAT FS

Steganography is considered to be not only a science, but also a craft of concealing ongoing communication by hiding messages in unsuspicious cover documents, such as texts, digital images, audio and video sequences. Its essential feature is the constant search for - often exceptionally creative - possibilities of concealing information. In computers, steganography often uses secondary memory and exchangeable memory media utilising file systems. This paper deals with the current state of the issues related to information hiding by means of hard disks, being the most important source of forensic data. This paper focuses on information hiding using the File Allocation Table (FAT) file system. It also proposes a novel multi-carrier algorithm of hiding information in file fragmentation. The algorithm provides flexibility of encoding the information to be hidden and makes steps toward optimization that allows reduction of interference with the current state of the file system, represented by the statistical values of the file fragmentation parameters

DNA Steganalysis Using Deep Recurrent Neural Networks

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods depend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution variations by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools