Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

Traceable and Authenticable Image Tagging for Fake News Detection

To prevent fake news images from misleading the public, it is desirable not only to verify the authenticity of news images but also to trace the source of fake news, so as to provide a complete forensic chain for reliable fake news detection. To simultaneously achieve the goals of authenticity verification and source tracing, we propose a traceable and authenticable image tagging approach that is based on a design of Decoupled Invertible Neural Network (DINN). The designed DINN can simultaneously embed the dual-tags, \textit{i.e.}, authenticable tag and traceable tag, into each news image before publishing, and then separately extract them for authenticity verification and source tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a parallel Feature Aware Projection Model (FAPM) to help the DINN preserve essential tag information. In addition, we define a Distance Metric-Guided Module (DMGM) that learns asymmetric one-class representations to enable the dual-tags to achieve different robustness performances under malicious manipulations. Extensive experiments, on diverse datasets and unseen manipulations, demonstrate that the proposed tagging approach achieves excellent performance in the aspects of both authenticity verification and source tracing for reliable fake news detection and outperforms the prior works

The enablers and implementation model for mobile KMS in Australian healthcare

In this research project, the enablers in implementing mobile KMS in Australian regional healthcare will be investigated, and a validated framework and guidelines to assist healthcare in implementing mobile KMS will also be proposed with both qualitative and quantitative approaches. The outcomes for this study are expected to improve the understanding the enabling factors in implementing mobile KMS in Australian healthcare, as well as provide better guidelines for this process

Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art

Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos

Enhancing Biometric Security: A Framework for Detecting and Preventing False Identification

Biometrics is a technological system that utilizes data to differentiate one individual from another. The biometric framework can be used by government and private organizations for security purposes. This software-based technology helps to look at an individual's data if it is genuine or fake. The study suggested a framework; its goal is to strengthen the development and acceptance of the biometric system. The function of this system is to reduce the applied effort to identify and recognize the quality of the image in less time. This study utilizes three data applications: iris, fingerprint, and face recognition. The approach proposed by the survey uses different features of the images to determine the difference between the original image and the considered sample image. It gives efficient protection against different spoofing attacks. Simulation results show that the high-quality detection application has an average peak signal-to-noise ratio (PNSR) of 89.77. Further, the proposed model effectively detects false biometric identification

A NEW TAMPER DETECTION ALGORITHM FOR VIDEO

Few years ago, authentication of visual media has regarded as a crucial research area which concerns with the development of methods and tools to determine if the digital media has been forged or not. The detection of video tampering was a concern of many researchers because of its important role in road accident, court events, and other applications. In this manuscript, a new low cost video forgery detection algorithm has been proposed by utilizing the correlation coefficients between the video frames and embedding them as an encrypted data into the first frame of the video stream. Experimental results show the high performance of the proposed algorithm regarding visual quality and robustness due to the ability to detect tampering even in simple and low-effect attacks

Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique

Copyright is a tool for preventing anyone forged to copy an electronic work from another person and claim that electronic work is referred to him. Since the identity of the person is always determined by his name and biometrics, there is a concern to handle this information, to preserve the copyright. In this paper, a new idea for copyright technology is used to prove video copyright, by using blind watermarking technique, the ownership information is hidden inside video frames using linear congruential generator (LCG) for adapted the locations of vector features extracted from the name and biometric image of the owner instead of hidden the watermark in the Pseudo Noise sequences or any other feature extraction technique. When providing the watermarked vector, a statistical operation is used to increase randomization state for the amplifier factors of LCG function. LCG provides random positions where the owner's information is stored inside the video. The proposed method is not difficult to execute and can present an adaptable imperceptibility and robustness performance. The output results show the robustness of this approach based on the average PSNR of frames for the embedded in 50 frames is around 47.5 dB while the watermark remains undetectable. MSSIM values with range (0.83 to 0.99)

Exploiting Alpha Transparency In Language And Vision-Based AI Systems

This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems. Our method uses this alpha layer as a clandestine channel invisible to human observers but fully actionable by AI image processors. The scope tested for the vulnerability spans representative vision systems from Apple, Microsoft, Google, Salesforce, Nvidia, and Facebook, highlighting the attack's potential breadth. This vulnerability challenges the security protocols of existing and fielded vision systems, from medical imaging to autonomous driving technologies. Our experiments demonstrate that the affected systems, which rely on convolutional neural networks or the latest multimodal language models, cannot quickly mitigate these vulnerabilities through simple patches or updates. Instead, they require retraining and architectural changes, indicating a persistent hole in multimodal technologies without some future adversarial hardening against such vision-language exploits