Earticle

Digital images are inseparable part of our life. Images are used at various places like medical imaging, crime scene investigation, forensic analysis, courts etc. but due to ubiquitous accessibility of image editing software, images are no longer trusted. Digital images are losing their credibility. For checking authenticity of digital images forgery detection methods are required. One of the most frequent image forgery is copy-move. In this forgery, a region of the original image is used for producing a manipulated image by performing post-processing operations over copied segment before pasting it to original image. The main principle of finding such type of forgery is based on finding resemblance present in different segments of image. Image is divided in blocks then feature vectors are extracted corresponding to different blocks of image. Sorting techniques are applied to find similarity between blocks. In case of natural images which may have similar regions, shift vectors are calculated to decrease false matches. Several methods are suggested by researchers to detect such type of forgery. In this paper, a survey on frequency-based methods is presented for detecting copy-move forgery in images.

This paper proposes three one-time password (OTP) generation methods suitable for firmware OTPs, one of the most representative methods for addressing the rapidly growing demand for user authentication in online services, based on stream and block ciphers. The first method is a stream cipher-based approach consisting of a 127-bit linear feedback shift register (LFSR) and exclusive-OR (XOR) operators. The OTP output bits are determined using a bit-position selection derived from the digits of  . The second method is a block cipherbased approach employing triple data encryption standard (TDES). Part of the output bits are used as the OTP output and the remaining bits are fed back to the input through an output feedback (OFB) mode. The third method adopts advanced encryption standard (AES) as the block cipher, using a portion of the output bits as the OTP output and feeding back a subset of the remaining bits to the input. All methods generate initial values through key-based random number generation applying message authentication code (MAC). The proposed methods are implemented on an Arduino platform as firmware-based OTP generators. Experimental results demonstrate that the proposed methods offer strong security properties and are suitable for firmwarebased OTP generation. In addition, the LFSR-based method shows good performance in the NIST SP 800-22 randomness tests.

Recently, in the shipbuilding industry, various smart quality management technologies integrating Fourth Industrial Revolution technologies have been actively researched, with particular emphasis on the use of 3D laser scanning technologies for the rapid acquisition of precise data. The raw 3D point cloud data generated by 3D laser scanners often contains incomplete information and noise due to various factors such as surface characteristics, measurement environment, and weather conditions. Consequently, post-processing of the data is essential to accurately extracting and analyzing the desired information. Accordingly, this study investigates post-processing methods and applications for the analysis of point cloud in ship blocks. Utilizing open-source algorithms, the research implements and evaluates the feasibility of 3D point cloud data processing and analysis techniques, including noise removal and plane recognition. Initially, the algorithms were applied to sample 3D point cloud data measured in a laboratory setting, and an examination of characteristics based on algorithm parameters confirmed their applicability. Based on these findings, a step-by-step assessment of each algorithm was conducted on point cloud data from ship blocks measured on-site. The results indicated the applicability of the algorithms, except for the DBSCAN algorithm, for the ship block point cloud data.

Recently, in the shipbuilding industry, various smart quality management technologies integrating Fourth Industrial Revolution technologies have been actively researched, with particular emphasis on the use of 3D laser scanning technologies for the rapid acquisition of precise data. The raw 3D point cloud data generated by 3D laser scanners often contains incomplete information and noise due to various factors such as surface characteristics, measurement environment, and weather conditions. Consequently, post-processing of the data is essential to accurately extracting and analyzing the desired information. Accordingly, this study investigates post-processing methods and applications for the analysis of point cloud in ship blocks. Utilizing open-source algorithms, the research implements and evaluates the feasibility of 3D point cloud data processing and analysis techniques, including noise removal and plane recognition. Initially, the algorithms were applied to sample 3D point cloud data measured in a laboratory setting, and an examination of characteristics based on algorithm parameters confirmed their applicability. Based on these findings, a step-by-step assessment of each algorithm was conducted on point cloud data from ship blocks measured on-site. The results indicated the applicability of the algorithms, except for the DBSCAN algorithm, for the ship block point cloud data.