Nowadays, object saliency detection has attracted considerable attention due to the vivid description of images. The traditional saliency detection methods are faced with the challenges of sparse boundary, fractured contour and internal non-uniform density. That will result in losing texture and detailed information of images, which makes a bad contribution to the subsequent object detection. Therefore, we propose a new approach based on fuzzy region competition and a Gaussian mixture segment model via a modified line segment detector (FGM-MLSD) for airport saliency detection in remote sensing images. First, we adopt fuzzy region competition, combining a Gaussian mixture model to segment the input images and obtain the airport candidate regions. After segmentation, a modified line segment detector (LSD) is used for extracting line features, which enhances the connection between broken lines and greatly improves the detected line quality. Then we can acquire the saliency map of the airport region. At last, we fuse the above saliency maps with the binarization map obtained by the Otsu method, aiming to eliminate the false alarm. Finally, abundant experiments are conducted, and the testing results reveal that the neoteric method can clearly and accurately extract the airport region in the remote sensing images and effectively improve the accuracy of saliency detection.

Blockchain is a decentralized, public, and distributed ledger designed to securely record and track transactions. It possesses the potential to transform various industries, including healthcare, supply chain management, and financial services, by enhancing transparency, efficiency, and trust. Despite its promise, blockchain development continues to face several challenges, particularly concerning security, scalability, and standardization. This paper provides a comprehensive analysis of blockchain technology, focusing on its quality of service (QoS), security mechanisms, and the latest frameworks and models shaping its evolution. Furthermore, it examines existing limitations and identifies key open research challenges that must be addressed for broader adoption. The findings suggest that blockchain can substantially improve operational efficiency and data integrity across multiple domains; however, realizing its full potential requires continued research and technological advancement to overcome current barriers.

Cloud computing provides flexible and on-demand access to computing and storage resources over the Internet. While cloud services offer multiple free and paid storage options, users are often unaware of their actual efficiency when accessing them through broadband and mobile data networks. This paper presents a Quality of Experience (QoE) assessment of four popular free cloud storage services - Alibaba Cloud, JioCloud, JazzDrive, and Dropbox - evaluated using both broadband and mobile networks. Using subjective user feedback and network performance metrics such as upload/download speed and jitter, we analyze user satisfaction when uploading and downloading different file types. The results reveal that Dropbox and JioCloud generally offer better QoE under broadband, while Alibaba Cloud performs consistently well under mobile data. These insights can help users, service providers, and smart city planners optimize cloud-based storage and data-sharing performance for improved digital experiences.

Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.

Internet of Things (IoT) in extreme environments inevitably produces incomplete multi-view data, presenting challenges to the existing data analysis methods. Although incomplete multi-view clustering methods have the potential to mine patterns of incomplete IoT data, they are still confronted with two challenges. 1) They ignore shifts of semantics caused by missing data in aggregating consistent and complementary information of incomplete data, degrading the robustness of models in pattern mining. 2) Most of them rely on the instances with complete views as pairwise supervision to capture correlations among views, failing to mine inherent patterns of data in the extreme view missing scenario where multi-view instances are only with an available view. To this end, a deep incomplete multi-view clustering network (DIMC) is proposed via defining dual consistencies within the information bottleneck framework to mine accurate patterns of incomplete data. Specifically, an unsupervised multi-view information bottleneck (MIB) is formulated to model dependencies of data, which remedies shifts of semantics via within-view intrinsic knowledge learning, consistent semantics sharing, and consistent structure aligning. Meanwhile, dual consistencies are designed to implement MIB, which builds invariant transformations to mine correlations between views without the help of complete instances. Finally, extensive experiments on four benchmark incomplete datasets demonstrate the superiority of DIMC. Especially, DIMC surpasses the state-of-the-art methods by 0.2048 in accuracy under extreme view missing scenarios.

In recent years, driven by the continuous development of mobile Internet technology and artificial intelligence technology, the improvement of the manufacturing level of 6G Internet-of-Everything (IoE) products and the increase in residents’ income level, the 6G IoE industry has shown a sustained and stable development trend. However, 6G IoE has great security risks. Network anomaly detection is very important for 6G IoE. The anomaly detection method based on traditional deep auto-encoder uses the reconstruction error to determine whether the sample to be measured is normal data or abnormal data. However, the reconstruction errors generated by the above method on normal data and abnormal data are very close, which leads to some abnormal data being easily misclassified as normal data. Therefore, an anomaly detection method based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G IoE is proposed. Firstly, the capsule graph network uses the bottleneck feature of the input sample to generate the bottleneck feature of the pseudo-abnormal data, so as to increase the abnormal data information in the training set. The capsule dynamic fusion strategy aggregates different factors to obtain new item embedding. Secondly, deep auto-encoder reconstructs the bottleneck characteristics with abnormal data information into normal data as much as possible, and increases the difference of reconstruction error between abnormal data and normal data. In the process of network classification, we use the sparrow search algorithm to find the optimal value of the function. And at the same time, it prevents the algorithm from prematurity and improves the classification effect. Finally, we conduct experiments on public data sets to compare with other advanced methods. Experimental results show that the proposed method can effectively enlarge the difference between normal data and abnormal data in reconstruction error.

With the wide deployment of the Internet of Things (IoT), large volumes of incomplete multiview data that violates data integrity is generated by various applications, which inevitably produces negative impacts on the quality of service of IoT systems. Incomplete multiview clustering (IMC), as an essential technique of data processing, has the potential for mining patterns of incomplete IoT data. However, previous methods utilize notion-strong distances that can only measure differences between distributions at the overlap of data manifolds in fusing complementary information of data for pattern mining. They may suffer from biased estimation and information loss in capturing intrinsic structures of incomplete multiview data. To address these challenges, a semidiscrete multiview optimal transport (SD-MOT) is defined for IMC, which utilizes distances with weak notions to capture intrinsic structures of incomplete multiview data. Specifically, IMC is recast as an equivalent optimal transport between continuous incomplete multiview data and discrete clustering centroids, to avoid the strict assumption on overlap between manifolds in pattern mining. Then, SD-MOT is instantiated as a deep incomplete contrastive clustering network to remedy biased estimation and information loss on intrinsic structures of incomplete multiview data. Afterwards, a variational solution to SD-MOT is derived to effectively train the network parameters for pattern mining. Finally, extensive experiments on four representative incomplete multiview datasets verify the superiority of SD-MOT in comparison with nine baseline methods.

Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data, which can capture the local structures from instance reconstructions and the global topology from adversarial games. Specifically, DMACCN is designed as an adversarial encoding-decoding architecture composed of the modality specific-encoder, the modality-common fusion network, the cycle-consistent modality-specific generator, and the modality-fusion discriminator, which can fully fuse complementary information of data. Then, an adversarial cycle-consistent loss is devised to guide the clustering pattern mining from complementary information of data, which can align semantics between modalities and capture clustering structures of instances. The two components collaborate in a seamless manner to capture accurate commercial patterns. Finally, extensive experimental results on four datasets show DMACCN greatly outperforms the comparison methods.

Summary With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can communicate with each other and share data easily. However, the IoT paradigm is prone to security concerns as many attackers try to hit the network and make it vulnerable. In this scenario, security concerns are the most important and to address them various models have been designed to overcome these security issues, but still there exist many emerging variants of botnet attacks such as Mirai, Persirai, and Bashlite that exploits the security breaches. This research article aims to investigate cyber security in the advent of B‐IDS, DDOS, and malware attacks. For this purpose, different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection. For performance measures, these algorithms have been tested on nine sensor devices over N‐BaIoT datasets to measure the security and accuracy of the intrusion detection system. The results show that the tree‐based algorithm achieved more than 99% accuracy which is quite higher as compared to other tested methods on the same sensor devices.

Abstract Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image‐based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego‐image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego‐image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state‐of‐the‐art schemes.

In the transmission of medical images, if the image is not processed, it is very likely to leak data and personal privacy, resulting in unpredictable consequences. Traditional encryption algorithms have limited ability to deal with complex data. The chaotic system is characterized by randomness and ergodicity, which has advantages over traditional encryption algorithms in image encryption processing. A novel V-net convolutional neural network (CNN) based on four-dimensional hyperchaotic system for medical image encryption is presented in this study. Firstly, the plaintext medical images are processed into 4D hyperchaotic sequence images, including image segmentation, chaotic system processing, and pseudorandom sequence generation. Then, V-net CNN is used to train chaotic sequences to eliminate the periodicity of chaotic sequences. Finally, the chaotic sequence image is diffused to change the raw image pixel to realize the encryption processing. Simulation test analysis demonstrates that the proposed algorithm has better effect, robustness, and plaintext sensitivity.

In this research paper, we present the first classification study to classify WeChat application service flow traffic (text messages, picture messages, audio call and video call traffic) classification and secondly to find out the effectiveness of big dataset and small dataset as well as to find out effective machine learning classifiers. We firstly capture WeChat traffic and then extract 44 features then we combine capture traffic to make full instance of dataset. Then we make reduce instances of dataset from the full instance of dataset to show the effectiveness of large dataset and small dataset. Then we execute well known machine learning classifiers. Using statistical test, we use Wilcoxon and Friedman statistical test for the datasets and ML classifiers to find more deeply its effectiveness. Experimental results show that reduce instance dataset show high accuracy result compared to full instance and C4.5 classifier perform effectively as compared to other classifiers.

Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE) level of end users. To assess the QoE of video compression, we conducted subjective (QoE) experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.