Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
With the technological advances in the areas of Machine-To-Machine (M2M) and\nDevice-To-Device (D2D) communication, various smart computing devices now integrate a set\nof multimedia sensors such as accelerometers, barometers, cameras, fingerprint sensors, gestures,\niris scanners, etc., to infer the environmental status. These devices are generally identified using\nradio-frequency identification (RFID) to transfer the collected data to other local or remote objects over\na geographical location. To enable automatic data collection and transition, a valid RFID embedded\nobject is highly recommended. It is used to authorize the devices at various communication phases.\nIn smart application devices, RFID-based authentication is enabled to provide short-range operation.\nOn the other hand, it does not require the communication device to be in line-of-sight to gain server\naccess like bar-code systems. However, in existing authentication schemes, an adversary may capture\nprivate user data to create a forgery problem. Also, another issue is the high computation cost. Thus,\nseveral studies have addressed the usage of context-aware authentication schemes for multimedia\ndevice management systems. The security objective is to determine the user authenticity in order to\nwithhold the eavesdropping and tracing. Lately, RFID has played a significant for the context-aware\nsensor management systems (CASMS) as it can reduce the complexity of the sensor systems, it can be\navailable in access control, sensor monitoring, real time inventory and security-aware management\nsystems. Lately, this technology has opened up its wings for CASMS, where the challenging issues\nare tag-anonymity, mutual authentication and untraceability. Thus, this paper proposes a secure\nhash-based RFID mechanism for CASMS. This proposed protocol is based on the hash operation with\nthe synchronized secret session-key to withstand any attacks, such as desynchronization, replay and\nman-in-the-middle. Importantly, the security and performance analysis proves that the proposed\nhash-based protocol achieves better security and performance efficiencies than other related schemes.\nFrom the simulation results, it is observed that the proposed scheme is secure, robust and less\nexpensive while achieving better communication metrics such as packet delivery ratio, end-to-end\ndelay and throughput rate....
Blind forensics of JPEG image tampering as a kind of digital image blind forensics\ntechnology is gradually becoming a new research hotspot in the field\nof image security. Firstly, the main achievements of domestic and foreign\nscholars in the blind forensic technology of JPEG image tampering were\nbriefly described. Then, according to the different methods of tampering and\ndetection, the current detection was divided into two types: double JPEG\ncompression detection and block effect inconsistency detection. This paper\nsummarized the existing methods of JPEG image blind forensics detection,\nand analyzed the two methods. Finally, the existing problems and future research\ntrends were analyzed and prospected to provide further theoretical\nsupport for the research of JPEG image blind forensics technology....
Merge mode can achieve a considerable coding gain because of reducing the cost of coding motion information in video codecs. However,\nthe simple adoption of the motion information from the neighbouring blocks may not achieve the optimal performance as the motion\ncorrelation between the pixels and the neighbouring block decreases with their distance increasing. To address this problem, the paper\nproposes a Euclidean distance-based weighted prediction algorithm as an additional candidate in the merge mode. First, several predicted\nblocks are generated by motion compensation prediction (MCP) with the motion information from available neighbouring blocks.\nSecond, an additional predicted block is generated by a weighted average of the predicted blocks above, where the weighted coefficient is\nrelated to Euclidean distances from the neighbouring candidate to the pixel points in the current block. Finally, the best merge mode is\nselected by the rate distortion optimization (RDO) among the original merge candidates and the additional candidate. Experimental\nresults show that, on the joint exploration test model 7.0 (JEM 7.0), the proposed algorithm achieves better coding performance than the\noriginal merge mode under all configurations including random access (RA), low delay B (LDB), and low delay P (LDP), with a slight\ncoding complexity increase. Especially for the LDP configuration, the proposed method achieves 1.50% bitrate saving on average....
Fire must be extinguished early, as it leads to economic losses and losses of precious lives.\nVision-based methods have many difficulties in algorithm research due to the atypical nature fire\nflame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false\npositive detection using spatial and temporal features based on deep learning from factory installed\nsurveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to\ndetect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted\nthe fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based\nConvolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local\nspatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of\nvariation, and MSE. This research proposed a new algorithm using global and local frame features,\nwhich is well presented object information to reduce false positive based on the deep learning method.\nExperimental results show that the false positive detection of the proposed algorithm was reduced\nto about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the\nproposed method has excellent false detection performance....
Reversible data hiding in JPEG images has become an important topic due to the prevalence\nand overwhelming support of the JPEG image format these days. Much of the existing work focuses\non embedding using AC (quantized alternating current coefficients) to maximize the embedding\ncapacity while minimizing the distortion and the file size increase. Traditionally, DC (quantized direct\ncurrent coefficients) are not used for embedding, due to the assumption that the embedding in DCs\ncause more distortion than embedding in ACs. However, for data analytic which extracts fine details\nas a feature, distortion in ACs is not acceptable, because they represent the fine details of the image.\nIn this paper, we propose a novel reversible data hiding method which efficiently embeds in the DC.\nThe propose method uses a novel DC prediction method to decrease the entropy of the prediction\nerror histogram. The embedded image has higher PSNR, embedding capacity, and smaller file size\nincrease. Furthermore, proposed method preserves all the fine details of the image....
Loading....