A Chaotic Compressed Sensing-Based Multigroup Secret Image Sharing Method for IoT With Critical Information Concealment Function

Wu, Wei; Peng, Haipeng; Tong, Fenghua; Li, Lixiang

doi:10.1109/jiot.2022.3204672

Cited by 5 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This novel decoding model in PSIS can not only solve the problem of the single-image reconstruction mode in the previous solution but also further expand the application scenarios of SIS schemes. For instance, the authors of [10] provided a PSIS scheme for traffic surveillance image management, the authors of [11] combined the Internet of Things with PSIS and proposed a secret image sharing scheme with a key information hiding function, and the authors of [12] applied PSIS to the industrial Internet.…”

Section: Introductionmentioning

confidence: 99%

Progressive Reconstruction on Region-Based Secret Image Sharing

Liu,

Sun,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

(k,n) threshold progressive secret image sharing (PSIS) has become an important issue in recent years. In (k,n) PSIS, a secret image is encrypted into n shadows such that k to n shadows can gradually reconstruct the secret image. Since an image can usually be divided into different regions in such a way that each region includes information with different importance levels, region-based PSIS has also been proposed where the reconstruction of different regions requires different thresholds on the shadow numbers. In this work, we propose new region-based (k,n) PSIS that achieves a novel reconstruction model, where all regions possess the property of (k,n) threshold progressive reconstruction, but the same number of shadows recovers a lower proportion of information in regions with a higher importance level. This new reconstruction model can further complete the application of region-based PSIS, where each region has an equal minimum threshold for reconstruction, and the difference in importance levels between regions can be reflected in the proportion of the recovered image using the same number of shadows. A theoretical analysis proves the correctness of the proposed scheme, and the experimental results from four secret images also show the practicality and effectiveness of the proposed scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Progressive Reconstruction on Region-Based Secret Image Sharing

Liu,

Sun,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…It could efficiently compress original signals during data sampling processes by rates that break through the Nyquist sampling theory. In this way, methods based on CS theory could significantly improve efficiency during data transmission and sharing by reducing bandwidth requirements, communication costs, or storage space [18][19][20]. In mobile devices and wireless sensor networks, compressed sensing also helps reduce energy consumption [21,22].…”

Section: Introductionmentioning

confidence: 99%

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

Wu,

Peng,

Zhu

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC’s capabilities in terms of security and robustness.

show abstract

SE-SIS: Shadow-Embeddable Lossless Secret Image Sharing for Greyscale Images

Xu,

Liu,

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

A Chaotic Compressed Sensing-Based Multigroup Secret Image Sharing Method for IoT With Critical Information Concealment Function

Cited by 5 publications

References 51 publications

Progressive Reconstruction on Region-Based Secret Image Sharing

Progressive Reconstruction on Region-Based Secret Image Sharing

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

SE-SIS: Shadow-Embeddable Lossless Secret Image Sharing for Greyscale Images

Contact Info

Product

Resources

About