Multi-source and Multi-feature Image Information Fusion Based on Compressive Sensing

Image fusion is a comprehensive information processing technique and its purpose is to enhance the reliability of the image via the processing of the redundant data among multiple images, improve the image definition and information content through fusion of the complementary information of multiple images so as to obtain the information of the objective or the scene in a more accurate, reliable and comprehensive manner. This paper uses the sparse representation method of compressive sensing theory, proposes a multi-source and multi-feature image information fusion method based on compressive sensing in accordance with the features of image fusion, performs sparsification processing on the source image with K-SVD algorithm and OMP algorithm to transfer from spatial domain to frequency domain and decomposes into low-frequency part and high-frequency park. Then it fuses with different fusion rules and the experimental results prove that the method of this paper is better than the traditional methods and it can obtain better fusion effects

Digital Image Recovery Based on Lifting Wavelet Transform

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack

Mobile-based Telemedicine Application using SVD and F-XoR Watermarking for Medical Images

منصة الخدمات الطبية عبارة عن تطبيق متنقل يتم من خلاله تزويد المرضى بتشخيصات الأطباء بناءً على المعلومات المستقاة من الصور الطبية. يجب ألا يتم تبديل محتوى هذه النتائج التشخيصية بشكل غير قانوني أثناء النقل ويجب إعادته إلى المريض الصحيح. في هذه المقالة، نقدم حلاً لهذه المشكلات باستخدام علامة مائية عمياء وقابلة للانعكاس وهشة استنادًا إلى مصادقة صورة المضيف. في الخوارزمية المقترحة، يتم استخدام الإصدار الثنائي من ترميز بوس_شوهوري _هوكوينجهام (BCH) للتقرير الطبي للمريض (PMR) والصورة الطبية الثنائية للمريض (PMI) بعد استخدام الغامض الحصري أو (F-XoR) لإنتاج العلامة الفريدة للمريض باستخدام مخطط المشاركة السرية (SSS). يتم استخدامه لاحقًا كعلامة مائية ليتم تضمينها في مضيف (PMI) باستخدام خوارزمية تحليل القيمة المفرد (SVD) العمياء القائمة على العلامة المائية. وهو حل جديد اقترحناه أيضًا بتطبيق SVD على صورة العلامة المائية العمياء. تحافظ الخوارزمية الخاصة بنا على مصادقة محتوى (PMI) أثناء النقل وملكية (PMR) للمريض لنقل التشخيص المصاحب فيما بعد إلى المريض الصحيح عبر تطبيق التطبيب عن بعد المحمول. يستخدم تقييم الخوارزمية لدينا علامات مائية مسترجعة توضح النتائج الواعدة لمقاييس الأداء العالية مقارنتا مع نتائج الاعمال السابقة في مقاييس الكشف عن التزوير وإمكانية الاسترداد الذاتي، مع قيمة 30NB PSNR، قيمة NC هي 0.99.A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s unique mark is used later as a watermark to be embedded into host PMI using blind watermarking-based singular value decomposition (SVD) algorithm. This is a new solution that we also proposed to applying SVD into a blind watermarking image. Our algorithm preserves PMI content authentication during the transmission and PMR ownership to the patient for subsequently transmitting associated diagnosis to the correct patient via a mobile telemedicine application. The performance of experimental results is high compare to previous results, uses recovered watermarks demonstrating promising results in the tamper detection metrics and self-recovery capability, with 30db PSNR, NC value is 0.99

Detección de manipulaciones en evidencia digital con fines forenses

En términos de evidencia digital, la imagen de la escena de un crimen es un elemento importante en un proceso legal; razón por la cual es indispensable que se garantice su cadena de custodia. Si la imagen es modificada con operaciones como eliminación, duplicidad o difuminado de objetos, debe existir un mecanismo que identifique la existencia de dicha manipulación. En este proyecto, se propone una solución a la cadena de custodia basada en una técnica conocida como marcado de agua. Con el propósito de validar la sensibilidad y efectividad del sistema propuesto, se aplican seis clases de manipulaciones a imágenes marcadas; encontrando una alta sensibilidad a manipulaciones pequeñas (ej. 0.25% del tamaño de la imagen), dado que en todos los casos fue identificada la manipulación. Con la solución propuesta, la autoridad legal puede confiar en la cadena de custodia de la evidencia digital.

Survey on relational database watermarking techniques

Digital watermarking has been in multimedia data use over the past years. Recently it has become applicable in relational database system not only to secure copyright ownership but also to ensure data contents integrity. Further, it is used in locating tampered and modified places. However, the watermarking relational database has its own requirements, challenges, attacks and limitations. This paper, surveys recent database watermarking techniques focusing on the importance of watermarking relational database, the difference between watermarking relational database and multimedia objects, the issues in watermarking relational database, type of attacks on watermarked database, classifications, distortion introduced and the embedded information. The comparative study shows that watermarking relational database can be an effective tool for copyright protection, tampered detection, and hacker tracing while maintaining the integrity of data contents. In addition, this study explores the current issues in watermarking relational database as well as the significant differences between watermarking multimedia data and relational database contents. Finally, it provides a classification of database watermarking techniques according to the way of selecting the candidate key attributes and tuples, distortion introduced and decoding methods used

A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %

AuSR2: Image watermarking technique for authentication and self-recovery with image texture preservation

This paper presents an image watermarking technique for authentication and self-recovery called AuSR2. The AuSR2 scheme partitions the cover image into 3 × 3 non-overlapping blocks. The watermark data is embedded into two Least Significant Bit (LSB), consisting of two authentication bits and 16 recovery bits for each block. The texture of each block is preserved in the recovery data. Thus, each tampered pixel can be recovered independently instead of using the average block. The recovery process may introduce the tamper coincidence problem, which can be solved using image inpainting. The AuSR2 implements the LSB shifting algorithm to increase the imperceptibility by 2.8%. The experimental results confirm that the AuSR2 can accurately detect the tampering area up to 100%. The AuSR2 can recover the tampered image with a PSNR value of 38.11 dB under a 10% tampering rate. The comparative analysis proves the superiority of the AuSR2 compared to the existing scheme

スマート農業のための信頼できるドキュメンテーションシステム

九州工業大学博士学位論文 学位記番号：情工博甲第373号 学位授与年月日：令和4年12月27日1 Introduction|2 Traditional Documentation|3 Blockchain-based Trust Management|4 Adopting Blockchain Method for Cocoa Farming Documentation|5 Implementation of Blockchain Concept into the Real Problem through a Simulation Case of Cocoa Production|6 Conclusion and Future Work九州工業大学令和4年