Face recognition is one of the most commonly used biometric features in the identification of people. In this article, a novel facial image recognition architecture is proposed with a novel image descriptor which is called as fully center symmetric dual cross pattern (FCSDCP) The proposed architecture consists of preprocessing, feature extraction and classification phases. In the preprocessing phase, discrete wavelet transform (DWT) and Neutrosophy are used together to calculate coefficients of the face images. The proposed FCSDCP extracts features. LDA, QDA, SVM and KNN are utilized as classifiers. 4 datasets were chosen to obtain experiments and the results of the proposed method were compared to other state of art image descriptor based methods and the results clearly shows that the proposed method is a successful method for face classification.

In this paper, a novel discrete complex fuzzy transform (DCFT) and the proposed DCFT based facial image recognition method is presented. The presented DCFT consists of histogram extraction, peak points of histogram calculation and images construction. 3 real and 3 complex images are constructed using DCFT. Also, 3 angular images and 3 vector image are calculated using the real and complex images. To create real and complex images, polynomial and smith fuzzy sets are used in this paper. Briefly, 12 image are constructed using DCFT. In order to demonstrate effect of the proposed DCFT, face images data sets and local binary pattern (LBP) are used to create facial image recognition method. In this method, LBP is applied on the each DCFT image and 12 x 256 size of feature are extracted. Also, maximum pooling is applied on this feature set to obtain 256 size of feature. In the classification phase, support vector machine (SVM) and k nearest neighborhood (KNN) classifiers are used. The comparisons clearly demonstrate that the proposed DCFT is increased facial image recognition capability.

Fragile watermarking techniques have been widely used in the literature for tampered areas localization and image authentication. In this study, a novel quantum-dot cellular automata based fragile watermarking method for tampered area localization using chaotic piecewise map is proposed. Watermark generation, embedding, extraction and tampered area localization phases are consisted of the proposed quantum dot cellular automata and chaos based fragile watermarking method. In the watermark generation phase, quantum dot cellular automata and piecewise map which is a chaotic map are utilized. A block based method is utilized as authentication values embedding and extraction phases. To detect tampered areas, generated watermark and extracted watermark are compared. Also, block counters are used to tamper detection. In order to evaluate this method, capacity, imperceptibility and image authentication ability were utilized as performance metrics and the results of these metrics clearly illustrated that the presented method is suitable for image authentication and tamper detection.

For solving the crimes committed on digital materials, they have to be copied. An evidence must be copied properly in valid methods that provide legal availability. Otherwise, the material cannot be used as an evidence. Image acquisition of the materials from the crime scene by using the proper hardware and software tools makes the obtained data legal evidence. Choosing the proper format and verification function when image acquisition affects the steps in the research process. For this purpose, investigators use hardware and software tools. Hardware tools assure the integrity and trueness of the image through write-protected method. As for software tools, they provide usage of certain write-protect hardware tools or acquisition of the disks that are directly linked to a computer. Image acquisition through write-protect hardware tools assures them the feature of forensic copy. Image acquisition only through software tools do not ensure the forensic copy feature. During the image acquisition process, different formats like E01, AFF, DD can be chosen. In order to provide the integrity and trueness of the copy, hash values have to be calculated using verification functions like SHA and MD series. In this study, image acquisition process through hardware-software are shown. Hardware acquisition of a 200 GB capacity hard disk is made through Tableau TD3 and CRU Ditto. The images of the same storage are taken through Tableau, CRU and RTX USB bridge and through FTK imager and Forensic Imager; then comparative performance assessment results are presented.

In this paper, a new data hiding algorithm based on pixel pairs using chaotic map is proposed. Data hiding scheme is created by applying modulo function to pixel pairs. In here, pseudo random number generator (PRNG) is obtained from chaotic maps. The PRNG is very important for this algorithm since the data hiding coefficients are chosen by PRNG. For example, if the coefficient is 0, subtraction operator is used between pixel pairs. If coefficient is 1, summary operator is used for selected pixel pairs. The proposed algorithm is evaluated by embedding different sized secret data into different test images. This method is compared with the determined studies in the literature and the obtained results is evaluated. In this study, special rules are also defined to pixels which have boundary values for resolve overflow/underflow problem. Minimal changes are performed to reach the desired value of the pixel values. According to the results obtained, the proposed algorithm has high visual quality, good running time, secure and high payload capacity.