EDA-78: A Novel Error Detection Algorithm for Lempel-Ziv-78 Compressed Data

Kwon, Beom; Gong, Myongsik; Lee, Sang Hoon

doi:10.1007/s11277-019-06979-7

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…impossible [3], [4]. In particular, in military systems where the LZ77 algorithm has been used to compress missioncritical data, such loss of header information may have a severe effect on the successful fulfillment of mission.…”

Section: Introductionmentioning

confidence: 99%

Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

2020

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The data sequence compressed using the Lempel-Ziv-77 (LZ77) algorithm comprises a series of fixed-length tuples. To decompress this data, it is essential to know the encoding parameters such as the tuple length used for the compression. If this essential information becomes unavailable owing to conditions such as loss of the header, it is difficult to accurately determine the parameters using the compressed data. In this study, we investigate the blind estimation of tuple length from the LZ77-compressed data when the header is unavailable. To this end, we propose a novel idea of utilizing the LZ77 image generated from the LZ77-compressed data. The LZ77 image exhibits unique patterns based on the image size. The correlation between the image size and tuple length is indicated by different patterns of vertical lines in the LZ77 image. By exploiting a convolutional neural network (CNN), we develop an iterative algorithm while generating LZ77 images with different size. The results of the experiment on a public database show that the LZ77 image plays the role of an extremely powerful visual feature descriptor, and the proposed iterative algorithm estimates the tuple length with 100% accuracy.

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Section: Introductionmentioning

confidence: 99%

Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

2020

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Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification

et al. 2020

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Owing to the widespread use of digital devices such as mobile phones and tablet PCs that are capable of easily viewing contents, the number of digital crimes committed using these digital devices has increased. One of the most common digital crimes is to hide the header information of the compressed data, which makes the user's data unusable. It is difficult to restore original data without the header because header contains the compression type. In this paper, we propose a Partial Gated Feedback Recurrent Neural Network (PGF-RNN) for the identification of lossless compression algorithms. We modify the gated recurrent units to improve the correlation of layers by grouping the fully-connected layers to effectively determine the characteristics of the compressed data. We emphasize on the temporal features, which consider a wide range of data, and spatial features from fully-connected layers to extract the feature vectors of each compression type. To improve the performance of the proposed PGF-RNN, we apply post-processing that considers the frequency of bit sequences on some compression types with similar compressed data. The proposed method is evaluated on 31 well-known lossless compression algorithms of the Association for Computational Linguistics dataset. The average top 1 accuracy of the proposed method is 92.63%. INDEX TERMS Compression type classification, deep learning, gated recurrent unit, lossless compression, partial gated feedback recurrent neural network, recurrent neural network.

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EDA-78: A Novel Error Detection Algorithm for Lempel-Ziv-78 Compressed Data

Cited by 2 publications

References 32 publications

Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification

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