Holographic Data Compression with JPEG Standard and Deep Learning

2019 ◽  
Author(s):  
Yang Gao ◽  
Shuming Jiao ◽  
Zhi Jin
Keyword(s):  
Deep Learning ◽  
Data Compression

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

scholarly journals Robust IoT time series classification with data compression and deep learning

2020 ◽  
Vol 398 ◽  
pp. 222-234 ◽  
Author(s):  
Joseph Azar ◽  
Abdallah Makhoul ◽  
Raphaël Couturier ◽  
Jacques Demerjian
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Compression ◽  
Time Series Classification

scholarly journals Seismic Data Compression using Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Emad B. Helal ◽  
Omar M. Saad ◽  
Ali G. Hafez ◽  
Yangkang Chen ◽  
Gamal M. Dousoky
Keyword(s):  
Deep Learning ◽  
Data Compression ◽  
Seismic Data ◽  
Seismic Data Compression

scholarly journals Deep learning for in situ data compression of large turbulent flow simulations

2020 ◽  
Vol 5 (11) ◽  
Author(s):  
Andrew Glaws ◽  
Ryan King ◽  
Michael Sprague
Keyword(s):  
Deep Learning ◽  
Turbulent Flow ◽  
Data Compression ◽  
Flow Simulations ◽  
In Situ Data

scholarly journals Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Nweso Emmanuel Nwogbaga ◽  
Rohaya Latip ◽  
Lilly Suriani Affendey ◽  
Amir Rizaan Abdul Rahiman
Keyword(s):  
Deep Learning ◽  
Data Compression ◽  
Data Reduction ◽  
Vehicular Networks ◽  
Learning Algorithm ◽  
Computation Offloading ◽  
Estimation Model ◽  
Processing Capability ◽  
Iot Devices ◽  
The Impact

AbstractWith the increasing level of IoT applications, computation offloading is now undoubtedly vital because of the IoT devices limitation of processing capability and energy. Computation offloading involves moving data from IoT devices to another processing layer with higher processing capability. However, the size of data offloaded is directly proportional to the delay incurred by the offloading. Therefore, introducing data reduction technique to reduce the offloadable data minimizes delay resulting from the offloading method. In this paper, two main strategies are proposed to address the enormous data volume that result to computation offloading delay. First, IoT Canonical Polyadic Decomposition for Deep Learning Algorithm is proposed. The main purpose of this strategy is to downsize the IoT offloadable data. In the study, the Kaggle-cat-and-dog dataset was used to evaluate the impact of the proposed data compression. The proposed method downsizes the data significantly and can reduce the delay due to network traffic. Secondly, Rank Accuracy Estimation Model is proposed for determining the Rank-1 value. The result of the proposed method proves that the proposed methods are better in terms of data compression compared to distributed deep learning layers. This method can be applied in smart city, vehicular networks, and telemedicine etc.

scholarly journals A Deep Learning Scheme for Efficient Multimedia Iot Data Compression

2022 ◽  
Author(s):  
Hassan Noura ◽  
Joseph Azar ◽  
Ola Salman ◽  
Raphaël Couturier ◽  
Kamel Mazouzi
Keyword(s):  
Deep Learning ◽  
Data Compression ◽  
Learning Scheme

Power Grid Cloud Resource Status Data Compression Method Based on Deep-learning

2019 ◽  
Vol 12 ◽  
Author(s):  
Weixuan Liang ◽  
Youchan Zhu ◽  
Guoliang Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Power Grid ◽  
Compression Algorithm ◽  
The Neural Network ◽  
Cloud Resource ◽  
Resource Data ◽  
Resource Status ◽  
Status Data

Background: As the "three-type two-net, world-class" strategy proposed, the number of cloud resources in power grid continues to grow, there is a large amount of data to be filed every day. Which are key issues to be addressed, the long-term preservation of data, using back-up data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges. Method: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization. The preprocessed data is then compressed by SaDE-MSAE. Result: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm. Conclusion:: The paper can complete the compressed data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes cloud resource data grid.

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