Hoda Onsi

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Prof. Dr. Hoda Mohamed Onsi, she was a vice dean for higher studies and research, Faculty of Computers and Information, Cairo University(2008-20014) Vice dean for societal and environmental affairs (2008). Chairperson of Information Technology Department, FCI, Cairo University, 2006-2008) Ph.D. Degree in computer engineering, Electronics and Communications Department, Faculty of Engineering, Cairo University, 1994. Acting Head of Department of Data Reception, Analysis, and Receiving Station Affairs, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt (1994- 2000). Her current research interests are image processing, virtual reality, remote sensing, GIS and information security.

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A Real Time Watermarking Algorithm for H.264

Near-Lossless Image Compression Based on Wavellet and Histogeam Packing

A Proposed Textual Graph Based Model for Arabic Multi-document Summarization

International Journal of Advanced Computer Science and Applications, 2016

Text summarization task is still an active area of research in natural language preprocessing. Se... more Text summarization task is still an active area of research in natural language preprocessing. Several methods that have been proposed in the literature to solve this task have presented mixed success. However, such methods developed in a multi-document Arabic text summarization are based on extractive summary and none of them is oriented to abstractive summary. This is due to the challenges of Arabic language and lack of resources. In this paper, we present a minimal languagedependent processing abstractive Arabic multi-document summarizer. The proposed model is based on textual graph to remove multi-document redundancy and generate coherent summary. Firstly, the original text, highly redundant and related multidocument, will be converted into textual graph. Next, graph traversal with structural rules will be applied to concatenate related sentences to single ones. Finally, unwanted and less weighted phrases will be removed from the summarized sentences to generate final summary. Preliminary results show that the proposed method has achieved promising results for multidocument summarization.

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Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

International Journal of Advanced Computer Science and Applications, 2021

Digital Image Forensics is a growing field of image processing that attempts to gain objective pr... more Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective. We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability. We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers' as it master the recent trends of CMFD and outline some future research directions.

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Spatial Cloud Detection and Retrieval System for Satellite Images

Abstract—In last the decade we witnessed a large increase in data generated by earth observing sa... more Abstract—In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties.Our approach is developed as spatial cloud detection and retrieval system (SCDRS) that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP) paradigm for the co...

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Real Time Watermarking of H.264 Video Sequences

Digital watermarking is intended to protect digital media against unauthorized usages. The waterm... more Digital watermarking is intended to protect digital media against unauthorized usages. The watermark is embedded in streamed videos for copyright protection purposes. In video streaming, the watermark embedding and detection time is the main concern. The distribution of the watermarking key is another important issue. The research aims to present a real time watermarking scheme for copyright protection in H.264 compressed videos. The proposed scheme is invisible, blind, robust, and preserves the original video bit rate. The watermarking process doesn’t require any key distribution. In this paper, a new H.264 model is introduced. The context adaptive variable length codes (CAVLC) blocks are statistically modeled. Based on the resulted CAVLC model, some of the CAVLC blocks are selected for watermarking using a user defined threshold. The watermark is embedded in the CAVLC block structure. In watermark embedding, the block’s total coefficients number (TCN) is modified. The simulation r...

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A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship

Spatial outliers are used to discover inconsistent objects producing implicit, hidden, and intere... more Spatial outliers are used to discover inconsistent objects producing implicit, hidden, and interesting knowledge, which has an effective role in decision-making process. In this paper, we propose a model to redefine the spatial neighborhood relationship by considering weights of the most effective parameters of neighboring objects in a given spatial data set. The spatial parameters, which are taken into our consideration, are distance, cost, and number of direct connections between neighboring objects. This model is adaptable to be applied on polygonal objects. The proposed model is applied to a GIS system supporting literacy project in Fayoum governorate.

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Designing a rule-based classifier using syntactical approach

International Journal of Remote Sensing, 2003

ABSTRACT

Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries

Information

With the advance of many image manipulation tools, carrying out image forgery and concealing the ... more With the advance of many image manipulation tools, carrying out image forgery and concealing the forgery is becoming easier. In this paper, the convolution neural network (CNN) innovation for image forgery detection and localization is discussed. A novel image forgery detection model using AlexNet framework is introduced. We proposed a modified model to optimize the AlexNet model by using batch normalization instead of local Response normalization, a maxout activation function instead of a rectified linear unit, and a softmax activation function in the last layer to act as a classifier. As a consequence, the AlexNet proposed model can carry out feature extraction and as well as detection of forgeries without the need for further manipulations. Throughout a number of experiments, we examine and differentiate the impacts of several important AlexNet design choices. The proposed networks model is applied on CASIA v2.0, CASIA v1.0, DVMM, and NIST Nimble Challenge 2017 datasets. We also ...

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Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction

Journal of Computer and Communications

Copy-move offense is considerably used to conceal or hide several data in the digital image for s... more Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.

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An Adaptive Multimodal Biometrics System using PSO

International Journal of Advanced Computer Science and Applications

Multimodal biometric systems which fuse information from a number of biometrics, are gaining more... more Multimodal biometric systems which fuse information from a number of biometrics, are gaining more attentions lately because they are able to overcome limitations in unimodal biometric systems. These systems are suited for high security applications. Most of the proposed multibiometric systems offer one level of security. In this paper a new approach for adaptive combination of multiple biometrics has been proposed to ensure multiple levels of security. The score level fusion rule is adapted using (PSO) Particle Swarm Optimization to ensure the desired system performance corresponding to the desired level of security. The experimental results prove that the proposed multimodal biometric system is appropriate for applications that require different levels of security.

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Ensemble Based-Feature Selection on Human Activity Recognition

Proceedings of the 10th International Conference on Informatics and Systems - INFOS '16, 2016

Regression-Based Feature Selection on Large Scale Human Activity Recognition

International Journal of Advanced Computer Science and Applications, 2016

In this paper, we present an approach for regression-based feature selection in human activity re... more In this paper, we present an approach for regression-based feature selection in human activity recognition. Due to high dimensional features in human activity recognition, the model may have over-fitting and can't learn parameters well. Moreover, the features are redundant or irrelevant. The goal is to select important discriminating features to recognize the human activities in videos. R-Squared regression criterion can identify the best features based on the ability of a feature to explain the variations in the target class. The features are significantly reduced, nearly by 99.33%, resulting in better classification accuracy. Support Vector Machine with a linear kernel is used to classify the activities. The experiments are tested on UCF50 dataset. The results show that the proposed model significantly outperforms state-of-the-art methods.

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Evaluation of feature selection on human activity recognition

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), 2015

Web Image Mining Age Estimation Framework

In this paper, we introduce a fully automated age estimation engine that is capable of collecting... more In this paper, we introduce a fully automated age estimation engine that is capable of collecting images using human age related text queries from Flickr photo sharing website that has various ancestry groups and different image qualities, 37000 images were downloaded from this step. We use the Active Shape Model for robust face detection; it acts also as a removal step for non-face images. After that, we use the bio-inspired features (BIF) to extract the facial aging information. We introduce a universal labeler algorithm to label Flickr images automatically. Finally, we use the web image collection as a training dataset, and the standard databases as testing datasets showing the superiority of our proposed image web mining algorithm, over the state-of-the-art methods.

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An adaptive watermarking approach for medical imaging using swarm intelligent

In this paper we present a secure patient medical images and authentication scheme which enhances... more In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

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Watermarking 3D Triangular Mesh Models Using Intelligent Vertex Selection

Advances in Intelligent Systems and Computing, 2016

Aboul Ella Hassanien: An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent

An adaptive watermarking approach based on weighted quantum particle swarm optimization

Neural Computing and Applications, 2015

ABSTRACT In this paper, we propose a novel optimal singular value decomposition (SVD)-based image... more ABSTRACT In this paper, we propose a novel optimal singular value decomposition (SVD)-based image watermarking approach that uses a new combination of weighted quantum particle swarm optimization (WQPSO) algorithm and a human visual system (HVS) model for both the hybrid discrete wavelet transform and discrete cosine transform (DCT). The proposed SVD-based watermarking approach initially decomposes the host image into sub-bands; afterwards, singular values of the DCT of the lower sub-band of the host image are quantized using a set of optimal quantization steps deduced from a combination of the WQPSO algorithm and the HVS model. To evaluate the performance of the proposed approach, we present tests on different images. The experimental results show that the proposed approach yields a watermarked image with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks, including JPEG compression, Gaussian noise, salt and pepper noises, Gaussian filters, median filters, image cropping, and image scaling. Moreover, the results of various experimental analyses demonstrated the superiority of the WQPSO approach over other optimization techniques, including classical PSO and QPSO in terms of local convergence speed, resulting in a better balance between global and local searches of the watermarking algorithm.

A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms

Advances in Intelligent Systems and Computing, 2015

Hoda Onsi

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