Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Symmetry in Chaotic Systems and Circuits

Symmetry can play an important role in the field of nonlinear systems and especially in the design of nonlinear circuits that produce chaos. Therefore, this Special Issue, titled “Symmetry in Chaotic Systems and Circuits”, presents the latest scientific advances in nonlinear chaotic systems and circuits that introduce various kinds of symmetries. Applications of chaotic systems and circuits with symmetries, or with a deliberate lack of symmetry, are also presented in this Special Issue. The volume contains 14 published papers from authors around the world. This reflects the high impact of this Special Issue

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT difusion: a robust data encryption approach

In recent years, numerous image encryption schemes have been developed that demonstrate diferent levels of efectiveness in terms of robust security and real-time applications. While a few of them outperform in terms of robust security, others perform well for real-time applications where less processing time is required. Balancing these two aspects poses a challenge, aiming to achieve efcient encryption without compromising security. To address this challenge, the proposed research presents a robust data security approach for encrypting grayscale images, comprising fve key phases. The frst and second phases of the proposed encryption framework are dedicated to the generation of secret keys and the confusion stage, respectively. While the level-1, level-2, and level-2 difusions are performed in phases 3, 4, and 5, respectively, The proposed approach begins with secret key generation using chaotic maps for the initial pixel scrambling in the plaintext image, followed by employing the Fibonacci Transformation (FT) for an additional layer of pixel shufing. To enhance security, Tribonacci Transformation (TT) creates level-1 difusion in the permuted image. Level-2 difusion is introduced to further strengthen the difusion within the plaintext image, which is achieved by decomposing the difused image into eight-bit planes and implementing XOR operations with corresponding bit planes that are extracted from the key image. After that, the discrete wavelet transform (DWT) is employed to develop secondary keys. The DWT frequency subband (high-frequency sub-band) is substituted using the substitution box process. This creates further difusion (level 3 difusion) to make it difcult for an attacker to recover the plaintext image from an encrypted image. Several statistical tests, including mean square error analysis, histogram variance analysis, entropy assessment, peak signal-to-noise ratio evaluation, correlation analysis, key space evaluation, and key sensitivity analysis, demonstrate the efectiveness of the proposed work. The proposed encryption framework achieves signifcant statistical values, with entropy, correlation, energy, and histogram variance values standing at 7.999, 0.0001, 0.0156, and 6458, respectively. These results contribute to its robustness against cyberattacks. Moreover, the processing time of the proposed encryption framework is less than one second, which makes it more suitable for realworld applications. A detailed comparative analysis with the existing methods based on chaos, DWT, Tribonacci transformation (TT), and Fibonacci transformation (FT) reveals that the proposed encryption scheme outperforms the existing ones

On the performance of helper data template protection schemes

The use of biometrics looks promising as it is already being applied in elec- tronic passports, ePassports, on a global scale. Because the biometric data has to be stored as a reference template on either a central or personal storage de- vice, its wide-spread use introduces new security and privacy risks such as (i) identity fraud, (ii) cross-matching, (iii) irrevocability and (iv) leaking sensitive medical information. Mitigating these risks is essential to obtain the accep- tance from the subjects of the biometric systems and therefore facilitating the successful implementation on a large-scale basis. A solution to mitigate these risks is to use template protection techniques. The required protection properties of the stored reference template according to ISO guidelines are (i) irreversibility, (ii) renewability and (iii) unlinkability. A known template protection scheme is the helper data system (HDS). The fun- damental principle of the HDS is to bind a key with the biometric sample with use of helper data and cryptography, as such that the key can be reproduced or released given another biometric sample of the same subject. The identity check is then performed in a secure way by comparing the hash of the key. Hence, the size of the key determines the amount of protection. This thesis extensively investigates the HDS system, namely (i) the the- oretical classication performance, (ii) the maximum key size, (iii) the irre- versibility and unlinkability properties, and (iv) the optimal multi-sample and multi-algorithm fusion method. The theoretical classication performance of the biometric system is deter- mined by assuming that the features extracted from the biometric sample are Gaussian distributed. With this assumption we investigate the in uence of the bit extraction scheme on the classication performance. With use of the the- oretical framework, the maximum size of the key is determined by assuming the error-correcting code to operate on Shannon's bound. We also show three vulnerabilities of HDS that aect the irreversibility and unlinkability property and propose solutions. Finally, we study the optimal level of applying multi- sample and multi-algorithm fusion with the HDS at either feature-, score-, or decision-level

College of Arts and Sciences

Cornell University Courses of Study Vol. 94 2002/200

Virginia Commonwealth University Courses

Listing of courses for the 2015-2016 academic year