A Partial Face Encryption in Real World Experiences Based on Features Extraction from Edge Detection

International Journal of Interactive Mobile Technologies (iJIM), Год журнала: 2023, Номер 17(07), С. 69 - 81

Опубликована: Апрель 5, 2023

User confidentiality protection is concerning a topic in control and monitoring spaces. In image, user's faces security with compound information, abused situations, participation on global transmission media real-world experiences are extremely significant. For minifying the counting needs for vast size of image info time needful to be address computationally. consequently, partial encryption user-face picked. This study focuses large technique that designed encrypt face slightly. Primarily, dlib utilizing detection. Susan one top edge detectors valuable localization characteristics marked edges, used extract features vectors from user faces. Moreover, relevance suggested generating key led crucial role improvement by producing them as difficult intruders. According PSNR values, recommended algorithms provided an adequate outcome encryption, they had lower encrypting duration larger impact.

Язык: Английский

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Face detection by using Haar Cascade Classifier

Wasit Journal of Computer and Mathematics Science, Год журнала: 2023, Номер 2(1), С. 1 - 5

Опубликована: Март 30, 2023

the Haar Cascade Classifier is a popular technique for object detection that uses machine-learning approach to identify objects in images and videos. In context of face detection, algorithm series classifiers are trained on thousands positive negative regions image may contain face. The multi-stage process involves collecting training data, extracting features, classifiers, building cascade classifier, detecting faces test image, post-processing results remove false positives negatives. has been shown be highly accurate efficient videos, but it some limitations, including difficulty under challenging lighting conditions or when partially occluded. Overall, remains powerful widely-used tool important carefully evaluate its performance specific each application consider using more advanced techniques necessary.

Язык: Английский

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Computer Literacy with Skills of Seeking for Information Electronically among University Students

International Journal of Interactive Mobile Technologies (iJIM), Год журнала: 2023, Номер 17(07), С. 47 - 57

Опубликована: Апрель 5, 2023

Computer literacy is an urgent necessity for university students, given the rapid development in means of communication which we live this era, and flow abundant information. Mainly on computer all administrative academic transactions, where first registration semester done through computer. culture has many characteristics advantages that distinguish it from other sciences, including concept cannot be defined absolutely, difficult to define its levels, because specifications computer-educated individual differ one another, time also, you find a luxury country What, necessary another country. In order measure know level among computerized scale (40) items with five multiple-choice alternatives were built. they have skills searching information electronically, (21) prepared, five-point Likert was adopted. Results showed therefore electronically at all.

Язык: Английский

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Adaptive HDR Image Blind Watermarking Approach Based on Redundant Discrete Wavelet Transform

International Journal of Interactive Mobile Technologies (iJIM), Год журнала: 2023, Номер 17(10), С. 136 - 154

Опубликована: Май 22, 2023

Remarkable success has been recorded in the usage of digital watermarking which is aimed at protecting intellectual property multimedia content. In this paper, a new tone mapping attack-resistant high dynamic range (HDR) image zero-watermarking algorithm proposed. extraction stable and invariant features are extracted for efficient through application redundant discrete wavelet transform (RDWT) to HDR image. The first step involves transforming HVS color space, RDWT implemented using V-channel so that LL sub-band contains strong structure contents obtained. second dividing into non-overlapping blocks, afterwards subjected process transformation use singular value decomposition (SVD) U matrix can be extracted. Third, an Auto-Regressive (AR) prediction technique was employed generating local relationship model comparison done facilitate production binary feature mask. fourth process, hybrid chaotic (HCM) used generate blended watermark security fortified. Lastly, computation effective zero-watermark achieved implementation exclusive-or operation on Based results, approach presented study demonstrated superior performance terms withstanding TM attacks other associated with processing.

Язык: Английский

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Real-time institution video data analysis using fog computing and adaptive background subtraction

Journal of Real-Time Image Processing, Год журнала: 2023, Номер 20(5)

Опубликована: Авг. 16, 2023

Язык: Английский

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A Review Work: Human Action Recognition in Video Surveillance Using Deep Learning Techniques

Informatics and Automation, Год журнала: 2024, Номер 23(2), С. 436 - 466

Опубликована: Март 28, 2024

Despite being extensively used in numerous uses, precise and effective human activity identification continues to be an interesting research issue the area of vision for computers. Currently, a lot investigation is done on themes like pedestrian recognition ways recognize people's movements employing depth data, 3D skeletal still picture or strategies that utilize spatiotemporal interest points. This study aims investigate evaluate DL approaches detecting video. The focus has been multiple structures activities use as their primary strategy. Based application, including identifying faces, emotion identification, action anomaly occurrence forecasts are divided into four different subcategories. literature carried several based these recognitions predicting behavior video surveillance applications. state art applications' techniques contrasted. paper also presents application areas, scientific issues, potential goals field DL-based recognition/detection.

Язык: Английский

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Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs

Опубликована: Сен. 15, 2024

Язык: Английский

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Content-based filtering algorithm in social media

Wasit Journal of Computer and Mathematics Science, Год журнала: 2023, Номер 2(1), С. 14 - 17

Опубликована: Март 30, 2023

Content-based filtering is a recommendation algorithm that analyzes user activity and profile data to provide personalized recommendations for content matches user's interests preferences. This widely used by social media platforms, such as Facebook Twitter, increase engagement satisfaction. The methodology of content-based involves creating based on recommending the interests. continually updates personalizes feedback, incorporates strategies promote diversity serendipity in recommendations. While has some limitations, it remains powerful tool arsenal offering efficient discovery experiences at scale.

Язык: Английский

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The role of digital content systems used in managing Arab academic scientific journals: An analytical study

Periodicals of Engineering and Natural Sciences (PEN), Год журнала: 2023, Номер 11(2), С. 232 - 232

Опубликована: Апрель 11, 2023

The current research aims to highlight the role of digital content management systems adopted in Arab scientific journals and indicate its features characteristics. As well as learning about available global for managing journals. researchers descriptive approach achieve objectives research, questionnaire was a data collection tool. distributed proportional random sample that included (30) who use three sites with two each site. are from OJS platform (Professor Humanities Social Sciences Al-Kufa Journal Mathematics Computer), ASJP (the Algerian Human Sciences, Review Scientific Technical Information), ARID (ARID International Arad technology). presented form tables graphs analyzed statistically based on several methods. came out results, most important which is website system (ASJP) represents best manage (OJS, ARID).

Язык: Английский

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A combined method based on CNN architecture for variation-resistant facial recognition

International journal of electrical and computer engineering systems, Год журнала: 2023, Номер 14(9), С. 993 - 1001

Опубликована: Ноя. 14, 2023

Identifying individuals from a facial image is technique that forms part of computer vision and used in various fields such as security, digital biometrics, smartphones, banking. However, it can prove difficult due to the complexity structure presence variations affect results. To overcome this difficulty, paper, we propose combined approach aims improve accuracy robustness recognition variations. end, two datasets (ORL UMIST) are train our model. We then began with pre-processing phase, which consists applying histogram equalization operation adjust gray levels over entire surface quality enhance detection features each image. Next, least important eliminated images using Principal Component Analysis (PCA) method. Finally, pre-processed subjected neural network architecture (CNN) consisting multiple convolution layers fully connected layers. Our simulation results show high performance approach, rates up 99.50% for ORL dataset 100% UMIST dataset.

Язык: Английский

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