A Novel Linguistic Steganography Based on Synonym Run-Length Encoding

Xiang, Lingyun; Wang, Xinhui; Yang, Ching-Nung; Liu, Peng

doi:10.1587/transinf.2016edp7358

Cited by 32 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done to reduce the number of incorrect detection made by the proposed scheme as the weight assigned to different input sources of detection will be eliminating the outliers. This phase considered two cases: (1) video (human pose analysis [24], person and visual identification [25][26][27], action recognition [28]) and (2) audio data collected from sensors (linguistic analysis [29,30]). The output of this phase is obtained on the basis of the following function:…”

Section: Phase 2: Ensemble Approach For Abnormality Trackingmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

show abstract

Section: Phase 2: Ensemble Approach For Abnormality Trackingmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…There is no doubt that the data collected by the underlying IoT is the basis of the upper-layer decision and the foundation for all applications, which requires efficient energy protocols [49]. In addition, the data protection and application become an unrealistic target if the data collected is wrong and untrustworthy, further leading to unnecessary energy costs [50,51]. However, traditional methods cannot solve this problem effectively and reliably.…”

Section: State Of the Art Of Mobile Edge-based Sensor-cloudsmentioning

confidence: 99%

When Sensor-Cloud Meets Mobile Edge Computing

Wang

Cao

et al. 2019

Sensors

View full text Add to dashboard Cite

Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.

show abstract

“…Linguistic steganography based on text modification takes advantage of equivalent linguistic transformations to slightly modify the text content to hide the secret message while preserving the meaning of the original text. The linguistic transformations include syntactic transformations [10,13], synonym substitutions [14][15][16][17], misspelled word substitutions [18] and so on. This type of linguistic steganography has high imperceptibility, but limited embedding capacity, as the alternative transformations in a text are always very rare.…”

Section: Introductionmentioning

confidence: 99%

Novel Linguistic Steganography Based on Character-Level Text Generation

et al. 2020

Self Cite

View full text Add to dashboard Cite

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down.

show abstract

A Novel Linguistic Steganography Based on Synonym Run-Length Encoding

Cited by 32 publications

References 18 publications

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

When Sensor-Cloud Meets Mobile Edge Computing

Novel Linguistic Steganography Based on Character-Level Text Generation

Contact Info

Product

Resources

About