TrustSys: Trusted Decision Making Scheme for Collaborative Artificial Intelligence of Things

Rathee, Geetanjali; Garg, Sahil; Kaddoum, Georges; Choi, Bong Jun; Hassan, Mohammad Mehedi; AlQahtani, Salman A.

doi:10.1109/tii.2022.3173006

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…We describe the process of selecting a suitable CRM mode for a color image from six mapping modes (from M 1 to M 6 ) using Algorithm 1. First, we calculate the Pearson correlation coefficients between the three channels according to Equation ( 6) and store the results in set K. Then, we calculate the maximum value K max and the second largest value K smax in set K. Subsequently, we determine the channels ch 1 and ch 2 corresponding to K max and the channels ch 3 and ch 4 corresponding to K smax based on Equation (7). After that, we search for the mappings M δ(1) and M δ(2) that satisfy f 1 (ch 1 ) = ch 2 or f 1 (ch 2 ) = ch 1 in the six mapping modes, where δ(1) ∈ {1, 2, 3, 4, 5, 6}, δ(2) ∈ {1, 2, 3, 4, 5, 6}, and δ(1) ̸ = δ (2).…”

Section: Channel Reference Mapping Establishmentmentioning

confidence: 99%

“…They are widely popular in the entertainment sector and play a pivotal role in the industrial domain. Industrial applications typically require high-quality transmission and storage of color images to support decision-making [7], quality control [8], and monitoring production processes [9]. However, safeguarding sensitive information contained within images presents challenges due to the complexity of industrial environments and the stringent requirements for accurate information transmission.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

He,

Cai

2024

Mathematics

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Reversible data hiding (RDH) is a technique that embeds secret data into digital media while preserving the integrity of the original media and the secret data. RDH has a wide range of application scenarios in industrial image processing, such as intellectual property protection and data integrity verification. However, with the increasing prevalence of color images in industrial applications, traditional RDH methods for grayscale images are inadequate to meet the requirements of image fidelity. This paper proposes an RDH method for color images based on channel reference mapping (CRM) and adaptive pixel prediction. Initially, the CRM mode for a color image is established based on the pixel variation correlation between the RGB channels. Then, the pixel local complexity context is adaptively selected using the CRM mode. Next, each pixel value is adaptively predicted based on the features and characteristics of adjacent pixels and reference channels, and then data is embedded by expanding the prediction error. Finally, we compare seven existing RDH algorithms on the standard image dataset and the Kodak dataset to validate the advantages of our method. The experimental results demonstrate that our approach achieves average peak signal-to-noise ratio (PSNR) values of 63.61 and 60.53 dB when embedding 20,000 and 40,000 bits of data, respectively. These PSNR values surpass those of other RDH methods. These findings indicate that our method can effectively preserve the visual quality of images even under high embedding capacities.

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Section: Channel Reference Mapping Establishmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

He,

Cai

2024

Mathematics

View full text Add to dashboard Cite

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“…On the one hand, whether it meets people's needs for functions, performance and other aspects. On the other hand, it also depends on whether the process can reach the quality level required by users or beyond the expected value [9][10]. Secondly, it depends on whether the product itself can meet the needs of consumers and what problems may exist in the use process.…”

Section: Product Perfection Testmentioning

confidence: 99%

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

2023

KME

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With the continuous development of artificial intelligence, more and more researchers begin to attach importance to the realization of perfection testing in products, especially the intelligent and simulation technology. In this paper, based on the combination of computer and robot, the simulation experiment on mechanical structure is carried out. This paper first introduces the important role of machine vision system in the process of defect detection of automobile parts, then expounds the method of feature recognition of automobile parts, related principles and key technical basic knowledge points, etc. Finally, a three-dimensional entity model simulating real products is constructed using MATLAB software, and its results are calculated and analyzed. The concepts of intelligence and simulation are described in detail. The test results show that, the accuracy of the model in detecting the degree of perfection of product parts is more than 90%, and the error rate is low. This shows that the testing performance of the model is excellent.

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“…AIoT has the potential to offer various new application services [ 7 , 8 ]. AI-based systems have been developed to provide real-time monitoring, analysis, and protection [ 9 , 10 ]. However, to effectively utilize AIoT, networks capable of processing large amounts of information quickly are necessary [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Qiu

Zhuang

et al. 2023

Sensors

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The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challenges in artificial intelligence of things (AIoT) applications owing to their limited flexibility. These challenges arise from the distributed nature of AIoT devices and the involved information, as well as the requirement for short end-to-end latency. To address these challenges, we propose a security authentication scheme that utilizes intelligent prediction mechanisms to detect spoofing attack. Our approach is based on a dynamic authentication method using long short term memory (LSTM), where the edge computing node observes and predicts the time-varying channel information of access devices to detect clone nodes. Additionally, we introduce a Savitzky–Golay filter-assisted high order cumulant feature extraction model (SGF-HOCM) for preprocessing channel information. By utilizing future channel attributes instead of relying solely on previous channel information, our proposed approach enables authentication decisions. We have conducted extensive experiments in actual industrial environments to validate our prediction-based security strategy, which has achieved an accuracy of 97%.

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TrustSys: Trusted Decision Making Scheme for Collaborative Artificial Intelligence of Things

Cited by 10 publications

References 23 publications

Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

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