Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

Al‐Wesabi, Fahd N.; Isk, Huda G.; ar,; Alamgeer, Mohammad; Ghilan, Mokhtar M.

doi:10.32604/iasc.2020.013782

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…With the rapid development of Internet communication technology, telemedicine has received more and more attention in the medical field, and the protection of medical data has become the primary problem to be solved in telemedicine. Traditional robust watermarking [1][2][3][4][5] and fragile/semi-fragile watermarking [6,7] will cause permanent distortion of the medical cover audio when extracting the watermark, which makes the doctor unable to diagnose effectively. As a kind of digital watermark, reversible watermark can effectively protect the integrity, authenticity and privacy of medical data; it uses the redundancy of cover audio to embed watermark such as patient privacy, hospital information into the cover audio.…”

Section: Introductionmentioning

confidence: 99%

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

Zhang¹,

Sun²,

Sun³

et al. 2022

Computers, Materials &Amp; Continua

149

112

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The leakage of medical audio data in telemedicine seriously violates the privacy of patients. In order to avoid the leakage of patient information in telemedicine, a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data. The scheme decomposes the medical audio into two independent embedding domains, embeds the robust watermark and the reversible watermark into the two domains respectively. In order to ensure the audio quality, the Hurst exponent is used to find a suitable position for watermark embedding. Due to the independence of the two embedding domains, the embedding of the second-stage reversible watermark will not affect the first-stage watermark, so the robustness of the first-stage watermark can be well maintained. In the second stage, the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding. Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db, additive white Gaussian noise (AWGN) of 20 db, low-pass filtering, resampling, re-quantization and other attacks, and has good imperceptibility.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

Zhang¹,

Sun²,

Sun³

et al. 2022

Computers, Materials &Amp; Continua

149

112

View full text Add to dashboard Cite

show abstract

“…Zero text watermarking is a smart algorithm that can be used, without any changes or modifications to the original text document, to embed the watermark data. Moreover, this technique can be used to generate data for a watermark in the contents of the given text [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

Duhayyim¹,

Alohali²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model. The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text. The proposed scheme is implemented using PHP with VS code IDE. The simulation results, using varying sizes of standard datasets, show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks. Comparison results with other baseline techniques show the added value of the proposed scheme.

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“…In particular, the use of DNNs have been found in lane line detection and traffic sign recognition in self-driving cars [3][4][5], Unmanned Aerial Vehicle (UAV)-based monitoring system [6], and even to the usage in the emerging technologies and framework such as big data [7] and blockchain [8] in the industrial network infrastructure. Despite their promising progress, recent research has shown examples of attacks on various intelligent systems [9,10]. In particular, it is known that DNNs included in those systems are vulnerable to adversarial attacks, which lead deep learning models to make incorrect predictions with high confidence.…”

Section: Introductionmentioning

confidence: 99%

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

Kim

Kang

Suryanto

et al. 2021

Sensors

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Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.

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Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

Cited by 4 publications

References 15 publications

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

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