Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

[EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151

Information Forensics and Security: A quarter-century-long journey

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends

Next-generation big data analytics: state of the art, challenges, and future research topics

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.This work was supported in part by the “Open3D: Collaborative Editing for 3D Virtual Worlds” [EPSRC (EP/M013685/1)], in part by the “Distributed Java Infrastructure for Real-Time Big-Data” (CAS14/00118), in part by eMadrid (S2013/ICE-2715), in part by the HERMES-SMARTDRIVER (TIN2013-46801-C4-2-R), and in part by the AUDACity (TIN2016-77158-C4-1-R). Paper no. TII-16-1

Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

In Shannon theory, semantic aspects of communication were identified but considered irrelevant to the technical communication problems. Semantic communication (SC) techniques have recently attracted renewed research interests in (6G) wireless because they have the capability to support an efficient interpretation of the significance and meaning intended by a sender (or accomplishment of the goal) when dealing with multi-modal data such as videos, images, audio, text messages, and so on, which would be the case for various applications such as intelligent transportation systems where each autonomous vehicle needs to deal with real-time videos and data from a number of sensors including radars. A notable difficulty of existing SC frameworks lies in handling the discrete constraints imposed on the pursued semantic coding and its interaction with the independent knowledge base, which makes reliable semantic extraction extremely challenging. Therefore, we develop a new lightweight hashing-based semantic extraction approach to the SC framework, where our learning objective is to generate one-time signatures (hash codes) using supervised learning for low latency, secure and efficient management of the SC dynamics. We first evaluate the proposed semantic extraction framework over large image data sets, extend it with domain adaptive hashing and then demonstrate the effectiveness of "semantics signature" in bulk transmission and multi-modal data

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images