scholarly journals Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1655
Author(s):  
Zhongze Lv ◽  
Ying Huang ◽  
Hu Guan ◽  
Jie Liu ◽  
Shuwu Zhang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Vital Role ◽  
Video Watermarking ◽  
Modulation Method ◽  
Quantization Index Modulation ◽  
Adaptive Video ◽  
Before And After ◽  
Blind Extraction ◽  
Dc Coefficient ◽  
Index Modulation

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

scholarly journals Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec

2020 ◽  
Vol 12 (1) ◽  
pp. 17
Author(s):  
Zhijun Wu ◽  
Rong Li ◽  
Panpan Yin ◽  
Changliang Li
Keyword(s):  
Transition Probability ◽  
Principal Component ◽  
Transition Probability Matrix ◽  
Support Vector ◽  
Quantization Index Modulation ◽  
Distribution Probability ◽  
Before And After ◽  
Security Of Network ◽  
Index Value ◽  
Index Modulation

Steganalysis is used for preventing the illegal use of steganography to ensure the security of network communication through detecting whether or not secret information is hidden in the carrier. This paper presents an approach to detect the quantization index modulation (QIM) of steganography in G.723.1 based on the analysis of the probability of occurrence of index values before and after steganography and studying the influence of adjacent index values in voice over internet protocol (VoIP). According to the change of index value distribution characteristics, this approach extracts the distribution probability matrix and the transition probability matrix as feature vectors, and uses principal component analysis (PCA) to reduce the dimensionality. Through a large amount of sample training, the support vector machine (SVM) is designed as a classifier to detect the QIM steganography. The speech samples with different embedding rates and different durations were tested to verify their impact on the accuracy of the steganalysis. The experimental results show that the proposed approach improves the accuracy and reliability of the steganalysis.

A Video Watermarking Technique Based on Pseudo-3-D DCT and Quantization Index Modulation

2010 ◽  
Vol 5 (4) ◽  
pp. 625-637 ◽  
Author(s):  
Hui-Yu Huang ◽  
Cheng-Han Yang ◽  
Wen-Hsing Hsu
Keyword(s):  
Video Watermarking ◽  
Quantization Index Modulation ◽  
Index Modulation

Compressive Sensing Audio Watermarking dengan Metode LWT dan QIM

2018 ◽  
Vol 6 (3) ◽  
pp. 405
Author(s):  
IRMA SAFITRI ◽  
NUR IBRAHIM ◽  
HERLAMBANG YOGASWARA
Keyword(s):  
Wavelet Transform ◽  
Compressive Sensing ◽  
Audio Watermarking ◽  
Quantization Index Modulation ◽  
Computationally Efficient ◽  
Black And White ◽  
Additional Information ◽  
Audio Files ◽  
Index Modulation ◽  
Mp3 Compression

ABSTRAKPenelitian ini mengembangkan teknik Compressive Sensing (CS) untuk audio watermarking dengan metode Lifting Wavelet Transform (LWT) dan Quantization Index Modulation (QIM). LWT adalah salah satu teknik mendekomposisi sinyal menjadi 2 sub-band, yaitu sub-band low dan high. QIM adalah suatu metode yang efisien secara komputasi atau perhitungan watermarking dengan menggunakan informasi tambahan. Audio watermarking dilakukan menggunakan file audio dengan format *.wav berdurasi 10 detik dan menggunakan 4 genre musik, yaitu pop, classic, rock, dan metal. Watermark yang disisipkan berupa citra hitam putih dengan format *.bmp yang masing-masing berukuran 32x32 dan 64x64 pixel. Pengujian dilakukan dengan mengukur nilai SNR, ODG, BER, dan PSNR. Audio yang telah disisipkan watermark, diuji ketahanannya dengan diberikan 7 macam serangan berupa LPF, BPF, HPF, MP3 compression, noise, dan echo. Penelitian ini memiliki hasil optimal dengan nilai SNR 85,32 dB, ODG -8,34x10-11, BER 0, dan PSNR ∞.Kata kunci: Audio watermarking, QIM, LWT, Compressive Sensing. ABSTRACTThis research developed Compressive Sensing (CS) technique for audio watermarking using Wavelet Transform (LWT) and Quantization Index Modulation (QIM) methods. LWT is one technique to decompose the signal into 2 sub-bands, namely sub-band low and high. QIM is a computationally efficient method or watermarking calculation using additional information. Audio watermarking was done using audio files with *.wav format duration of 10 seconds and used 4 genres of music, namely pop, classic, rock, and metal. Watermark was inserted in the form of black and white image with *.bmp format each measuring 32x32 and 64x64 pixels. The test was done by measuring the value of SNR, ODG, BER, and PSNR. Audio that had been inserted watermark was tested its durability with given 7 kinds of attacks such as LPF, BPF, HPF, MP3 Compression, Noise, and Echo. This research had optimal result with SNR value of 85.32 dB, ODG value of -8.34x10-11, BER value of 0, and PSNR value of ∞.Keywords: Audio watermarking, QIM, LWT, Compressive Sensing.

Improving security of quantization-index-modulation steganography in low bit-rate speech streams

2013 ◽  
Vol 20 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Hui Tian ◽  
Jin Liu ◽  
Songbin Li
Keyword(s):  
Quantization Index Modulation ◽  
Bit Rate ◽  
Low Bit Rate ◽  
Index Modulation

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

scholarly journals Caregiver Burden: Caregiving Workshops Have a Positive Impact on Those Caring for Individuals With Dementia in Arkansas

2021 ◽  
Vol 8 ◽  
pp. 237437352110180
Author(s):  
Robin E. McAtee ◽  
Laura Spradley ◽  
Leah Tobey ◽  
Whitney Thomasson ◽  
Gohar Azhar ◽  
...  
Keyword(s):  
Caregiver Burden ◽  
Health Care Providers ◽  
Positive Impact ◽  
Educational Programs ◽  
Vital Role ◽  
Complex Care ◽  
Care Providers ◽  
Dementia Caregivers ◽  
Before And After ◽  
Burden Scale

Millions of Americans live with dementia. Caregivers of this population provide countless hours of multifaceted, complex care that frequently cause unrelenting stress which can result in immense burden. However, it is not fully understood what efforts can be made to reduce the stress among caregivers of persons with dementia (PWD). Therefore, the aim of this pretest–posttest designed study was to evaluate changes in caregiver burden after providing an educational intervention to those caring for PWD in Arkansas. Forty-one participants completed the Zarit Caregiver Burden Scale before and after attending a 4-hour dementia-focused caregiving workshop. The analysis of the means, standard deviations, and paired t tests showed that there was an increase in the confidence and competence in caring for PWD 30 to 45 days after attending the workshop. Health care providers need to understand both the vital role caregivers provide in managing a PWD and the importance of the caregiver receiving education about their role as a caregiver. Utilizing caregiver educational programs is a first step.

Development of two dosimetry systems based on basic violet dye for possible use as radiation dosimeters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nehad Magdy ◽  
Sameh Gafar
Keyword(s):  
Gamma Ray ◽  
Raw Materials ◽  
Absorbed Dose ◽  
Chemical Yield ◽  
Radiation Sensitivity ◽  
Vital Role ◽  
Radiation Chemical ◽  
Content Type ◽  
Before And After

Purpose The purpose of this research paper is to study a comparison between two dosimetry systems, both of them based on basic violet dye (BV). Design/methodology/approach The first system depends on (BV) (incorporating polyvinyl alcohol) as a thin-film dosimeter. The second system also relies on (BV) as a solution dosimeter, which is more sensitive to gamma rays. The two prepared film/solutions have a considerable signal that decreases upon irradiation and the strength of the signal decreases with increasing radiation dose. Findings The gamma ray absorbed dose for these dosimeters was found to be up to 35 kGy for films and 1 kGy for the liquid phase. All dosimetric characteristics as radiation chemical yield, additive substance, dose-response function, radiation sensitivity, also before and after-irradiation stability under various conditions were considered. Practical implications It is expected the vital role of gamma radiation on this dye in its two forms or two media. This reveals their wide applications in the field of gamma irradiation processing. Originality/value These two dosimetry systems which depend upon the same dye are safe to handle, inexpensive, available raw materials and can be applied in various dosimetry applications as mentioned above.

scholarly journals Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating

2020 ◽  
Author(s):  
Hongfei Xu ◽  
Deyi Xiong ◽  
Josef van Genabith ◽  
Qiuhui Liu
Keyword(s):  
Machine Translation ◽  
Contextual Information ◽  
Computational Cost ◽  
Representation Learning ◽  
Vital Role ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Gating Mechanism ◽  
Sentence Level ◽  
Parallel Data

Existing Neural Machine Translation (NMT) systems are generally trained on a large amount of sentence-level parallel data, and during prediction sentences are independently translated, ignoring cross-sentence contextual information. This leads to inconsistency between translated sentences. In order to address this issue, context-aware models have been proposed. However, document-level parallel data constitutes only a small part of the parallel data available, and many approaches build context-aware models based on a pre-trained frozen sentence-level translation model in a two-step training manner. The computational cost of these approaches is usually high. In this paper, we propose to make the most of layers pre-trained on sentence-level data in contextual representation learning, reusing representations from the sentence-level Transformer and significantly reducing the cost of incorporating contexts in translation. We find that representations from shallow layers of a pre-trained sentence-level encoder play a vital role in source context encoding, and propose to perform source context encoding upon weighted combinations of pre-trained encoder layers' outputs. Instead of separately performing source context and input encoding, we propose to iteratively and jointly encode the source input and its contexts and to generate input-aware context representations with a cross-attention layer and a gating mechanism, which resets irrelevant information in context encoding. Our context-aware Transformer model outperforms the recent CADec [Voita et al., 2019c] on the English-Russian subtitle data and is about twice as fast in training and decoding.

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