In edge-cloud collaborative IIoT systems, heterogeneous data generated by industrial devices are typically categorized into hot data and cold data according to their access frequency and timeliness requirements. However, existing data integrity verification schemes usually adopt a unified auditing mechanism, which fails to accommodate the distinct performance and security requirements of hot and cold data, and cannot effectively support dynamic data migration across edge and cloud layers. To address these challenges, this paper proposes a hierarchical and lightweight data integrity auditing scheme for edge-cloud collaborative IIoT environments. Specifically, for frequently accessed and latency-sensitive hot data stored at edge nodes, a lightweight auditing mechanism based on ZSS signatures is designed to enable efficient verification under resource constraints. For cold data stored in the cloud, a certificateless signature-based auditing model is introduced to eliminate key escrow risks while reducing certificate management overhead. Furthermore, to support the dynamic evolution of data states during the IIoT data lifecycle, a sampling-based data migration mechanism is proposed, which enables secure migration between edge and cloud layers without downloading the complete data, while preserving audit continuity and verifiability. Security analysis demonstrates that the proposed scheme ensures audit correctness and effectively resists forgery attacks. Extensive performance evaluation demonstrates that, compared with existing schemes, the proposed approach significantly reduces both computational and communication overhead, particularly under large-scale data blocks and high-frequency auditing scenarios.

Image captioning has been widely studied by the computer vision and natural language processing communities. However, conventional image captioning models are mainly built upon 2D images with narrow field-of-views. To comprehensively analyze what happens in real scenes, using 360° cameras to capture 360° images has been a popular research trend. However, 360° image captioning is rarely studied due to the lack of related datasets. To bridge the research gap, we introduce a novel dataset for 360° image captioning, namely 360IC (360° Image Captioning). It contains 1250 360° images from rich scenes, and each image is manually labeled with at least three detailed descriptions, which will greatly promote the research of 360° image captioning. We also propose a Multi-View Transformer Network (MVTransNet) for 360° image captioning based on multi-view analysis and fusion, which deals with the characteristics of large resolution, wide field-of-view and complex visual content of 360° images. Specifically, it builds a hierarchical architecture to model the spatial dependency of images with larger content, thus forming rich visual features of 360° images and making the generated descriptions more accurate. Extensive experiments on 360IC show that the proposed network outperforms other competing methods considerably. Our dataset will be released soon.

ABSTRACT Currently, sharing digital content significantly enhances the value of data, and data purchasing serves as a means of realizing this value after sharing. After a data purchase transaction occurs, although the ownership of the data has been successfully transferred, this process introduces two major challenges: first, maintaining the continuity of cloud data integrity verification after ownership transfers, and second, enabling full‐lifecycle traceability of data to clarify copyright attribution. To address these issues, this paper proposes a public auditing scheme that supports both cloud data ownership transfer and data traceability. First, the proposed scheme introduces an update factor as a mathematical structure to enable the update of HVT (homomorphic verifiable tag) on the cloud. Meanwhile, the CS (cloud service) performs tag update computations, thereby ensuring security while reducing the computational and communication overhead for local users. Furthermore, to achieve transparency throughout the data's lifecycle, copyright information of digital content is embedded into blockchain transactions. A data traceability strategy is then designed leveraging a chameleon hash function, and a detailed traceability process is presented. Security analysis demonstrates that our scheme satisfies correctness and reliability, and a series of comparative experiments further validate its feasibility and efficiency in practical applications.

ABSTRACT Smoke often appears before visible flames in the early stages of fire disasters, making accurate pixel‐wise detection essential for fire alarms. Although existing segmentation models effectively identify smoke pixels, they generally treat all pixels within a smoke region as having the same prior probability. This assumption of rigidity, common in natural object segmentation, fails to account for the inherent variability within smoke. We argue that pixels within smoke exhibit a probabilistic relationship with both smoke and background, necessitating density estimation to enhance the representation of internal structures within the smoke. To this end, we propose enhancements across the entire network. First, we improve the backbone by adaptively integrating scene information into texture features through separate paths, enabling smoke‐tailored feature representation for further exploit. Second, we introduce a texture‐aware head with long convolutional kernels to integrate both global and orientation‐specific information, enhancing representation for intricate smoke structure. Third, we develop a dual‐task decoder for simultaneous density and location recovery, with the frequency‐domain alignment in the final stage to preserve internal smoke details. Extensive experiments on synthetic and real smoke datasets demonstrate the effectiveness of our approach. Specifically, comparisons with 17 models show the superiority of our method, with mean IoU improvements of 4.88%, 2.63%, and 3.17% on three test sets. (The code will be available on https://github.com/xia‐xx‐cv/TANet_smoke ).

The cloud’s storage and query of private information have the cryptographic scholar due to the proliferation of cloud computing. In the traditional query mode, the private information stored in the cloud is at risk of being leaked. In order to solve this problem, a cloud ciphertext database system based on homomorphic encryption is a valid workaround. This paper presents a new cloud ciphertext database system model, which is based on the existing ciphertext database mode research and homomorphic properties. This paper also implements a ciphertext database system based on a CRT-based additive homomorphic scheme according to the model. Through theoretical analysis, the model is CPA-level safe and correct. The experimental results show that users can correctly query and download the data in the ciphertext database on the untrusted cloud server through the model, and it has efficiency advantages.

Since the turn of the century, hundreds of image watermarking algorithms based on histogram features have been reported. However, all the existing watermark embedding was based on binary embedding, and they only store two situations on two continuous bins, i.e., {0,1}. In this paper, we improve the existing embedding algorithms and propose a novel high-capacity watermarking algorithm by using two bins histogram modification. The new algorithm is based on the ternary numeral system, and watermark information is divided into three cases {0,1,2}. Specifically, we extract the histogram of the cover image first, then select the appropriate embedding range by two predefined thresholds, and last form the bin groups contained reasonable number of pixels. In the generation of watermark, we transfer the watermark information into a digital string W = w1w2... wt, wi∈ (0,1,2),i = 1,2,...,t. In the embedding algorithm, if wi= 2, we will adjust the height of two continuous bins, and let b'/a' ≥ T; if wi= 1, let a'/b' ≥ T; if wi= 0, let |a'-b'| ≤ 1, here a' and b' represent the height of the front and back bin, and T is the threshold. Experimental results show that the embedding capacity of the proposed watermarking algorithm is 60% higher than that of the existing algorithm. In addition, the new scheme can resist traditional geometric attacks.

This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.