A Hybrid Novel Approach of Video Watermarking

Kumar, Manoj; Sriastava, Sangeet; Hensman, Arnold

doi:10.14257/ijsip.2016.9.10.35

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Observation was conducted based on factors such as accuracy, classification report, and confusion matrix. KNN [33][34][35][36][37][38][39][40][41] has benefits such as straightforwardness of execution, simplicity of debugging, and robustness; this method also showed a few limits, such as a poor computational time for enormous datasets and affectability towards duplicity, as we found that the least precision was found with the NB model [42]. These results could be better with some adjustment, smoothing, and preprocessing in the dataset.…”

Section: Resultsmentioning

confidence: 68%

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An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

et al. 2022

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Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.

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Section: Resultsmentioning

confidence: 68%

“…In [40,41], the left and the right datasets are used to perform a comparison between SVM, CNN, KNN, and RFC and achieve the highest accuracy of 77.82% using CNN (which took maximum execution time) on left and right gesture datasets.…”

Section: Resultsmentioning

confidence: 99%

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

et al. 2022

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“…This indicates the superiority of our proposed forgery detection approach in comparison with other recent research works in the literature. Table 6, below, shows the qualitative comparison of our proposed approach with state-of-the-art approaches: (1) Liu et al [43], (2) Amiri et al [44], (3) Kukreja et al [45], (4) Kumar and Hensman [46], and (5) Kumar et al [47]. The results of this table indicate that the proposed approach has more cons (or positive characteristics) than the other compared approaches.…”

Section: Third Group Of Experiments (Comparative Analysis)mentioning

confidence: 97%

A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

2022

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The usage of images in different fields has increased dramatically, especially in medical image analysis and social media. Many risks can threaten the integrity and confidentiality of digital images transmitted through the internet. As such, the preservation of the contents of these images is of the utmost importance for sensitive healthcare systems. In this paper, the researchers propose a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. It employs a visual cryptography-based watermarking approach to provide the capabilities of forgery detection and localization. In this watermarking scheme, features and key and secret shares are generated. The feature share is constructed by extracting features from equal-sized blocks of the image by using a Walsh transform, a local binary pattern and a discrete wavelet transform. Then, the key share is generated randomly from each image block, and the secret share is constructed by applying the XOR operation between the watermark, feature share and key share. The CASIA V 1.0 and SIPI datasets were used to check the performance and robustness of the proposed method. The experimental results from these datasets revealed that the percentages of the precision, recall and F1 score classification indicators were approximately 97% for these indicators, while the percentages of the TAF and NC image quality indicators were approximately 97% and 96% after applying several known image processing and geometric attacks. Furthermore, the comparative experimental results with the state-of-art approaches proved the robustness and noticeable improvement in the proposed approach for the detection and localization of image forgeries in terms of classification and quality measures.

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“…On the other hand, the transform domain changes the image coefficient's frequencies of the cover (e.g., wavelet, discrete cosine, or Fourier). Watermarks are directly embedded in the pixels of video frames using spatial domain technique, whereas the latter approach adjusts the transform coefficients of video frames and embeds the data into it.In comparison to spatial domain approaches, frequency domain techniques are also undetectable [5]. Assessing their quality is the main purpose of comparing the cover and stego photos.…”

Section: Introductionmentioning

confidence: 99%

Improve a secure blind watermarking technique for digital video

Al-Kadei¹,

Hasan²

2022

PEN

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In recent years, digital watermark video has become increasingly popular in a range of industries, but because it is widely available on the Internet, it is simple to make unlawful copies and tamper with digital video. Watermarking digital video has become more popular as a method of detecting changes and preventing illegal duplication. This paper presents a video copyright protection system that is secure, blind, and robust. Two approaches that are resistant to diverse attacks are proposed in this research. The initial step is to encrypt a hybrid watermark (Message, Image) using two different encryption techniques (RSA and AES). The second is a steganography technique based on LSB-based robust watermarking, which embeds an encrypted secret bit Message and Image in key frames of an MP4 video file by utilizing the mean of the grayscale images that differ and take the most significant differences between the two images. The intensity of the histogram will become more consistent across all pixels as the encryption quality improves. For keyframe watermarking in the spatial domain, the proposed methods can maintain the watermarked information while achieving high imperceptibility and a Peak Single to Noise Ratio [PSNR] equivalent to more than 50 db, where quality measures (MSE, PSNR, and correlation coefficient) that calculate the levels of distortion caused by embedding a watermark in digital video produce good results.

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A Hybrid Novel Approach of Video Watermarking

Abstract: The rapid evaluation of image manipulation and tampering has evoked the need for protecting digital data.

Cited by 6 publications

References 12 publications

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

Improve a secure blind watermarking technique for digital video

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