The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
인터넷을 통해 교환되는 아랍어 텍스트의 보안과 신뢰성은 연구 커뮤니티에서 어려운 분야가 되었습니다. 아랍어 텍스트는 악의적인 공격에 의한 수정에 매우 민감하며 아랍어의 구문을 나타내며 의미를 다르게 만들 수 있는 발음 구별 부호(예: Fat-ha, Kasra 및 Damma)를 쉽게 변경할 수 있습니다. 본 논문에서는 아랍어 텍스트의 콘텐츠 인증 및 변조 탐지를 위해 자연어 처리 및 제로 워터마킹 접근법(HNLPZWA)의 하이브리드를 제안했습니다. HNLPZWA 접근 방식은 워터마크 키를 포함하기 위해 원본 텍스트 문서를 변경하지 않고 논리적으로 워터마크를 포함하고 감지합니다. 연구자들이 제안한 이전 문헌의 변조 탐지 정확도 문제를 개선하기 위해 은닉 마르코프 모델을 기반으로 한 5차 단어 순서 메커니즘을 디지털 제로 워터마킹 기술과 통합했습니다. 아랍어 텍스트를 분석하기 위한 자연어 처리 기법으로 Markov 모델의 Fifth-level order를 사용한다. 또한, 텍스트의 문맥간 상호관계의 특징을 추출하여 추출된 특징을 워터마크 정보로 활용하고, 추후 공격받은 아랍어 텍스트에 대해 검증하여 변조가 발생했는지 탐지합니다. HNLPZWA는 VS 코드 IDE와 함께 PHP를 사용하여 구현되었습니다. HNLPZWA의 변조 탐지 정확도는 실험 데이터 세트의 삽입, 재정렬 및 삭제 공격의 여러 무작위 위치에서 다양한 길이의 4개 데이터 세트를 사용한 실험을 통해 입증되었습니다. 실험 결과는 HNLPZWA가 높은 수준의 변조 감지 정확도로 모든 종류의 변조 공격에 더 민감하다는 것을 보여줍니다.
Fahd N. AL-WESABI
King Khalid University,Sana'a University
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부
Fahd N. AL-WESABI, "Proposing High-Smart Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 10, pp. 2104-2112, October 2020, doi: 10.1587/transinf.2020EDP7011.
Abstract: The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7011/_p
부
@ARTICLE{e103-d_10_2104,
author={Fahd N. AL-WESABI, },
journal={IEICE TRANSACTIONS on Information},
title={Proposing High-Smart Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet},
year={2020},
volume={E103-D},
number={10},
pages={2104-2112},
abstract={The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.},
keywords={},
doi={10.1587/transinf.2020EDP7011},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Proposing High-Smart Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet
T2 - IEICE TRANSACTIONS on Information
SP - 2104
EP - 2112
AU - Fahd N. AL-WESABI
PY - 2020
DO - 10.1587/transinf.2020EDP7011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2020
AB - The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.
ER -