Rehan Ashraf

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.

A survey on block based copy move image forgery detection techniques

In todays modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Forensic analysis of copy-move forgery in digital images using the stationary wavelets

In this modern life, the digital images are being considered the main source of information sharing. The digital images are being used in various fields such as medical imaging, news reporting, crime investigation, insurance claims etc. However, in the presence of sophisticated image editing tools, the credibility of digital images is the main concern. The copy-move forgery (CMF) is a most popular forgery attack. In CMF, a region is duplicated elsewhere in the same image for producing a forged image. Thus, for detecting this particular artifact an algorithm is presented that utilizes Stationary Wavelet Transform (SWT). The experimental results demonstrate that the algorithm is capable of detecting duplicated blocks precisely and identify multiple CMF effectively, even when the images are contaminated by blurring and noise. The performance of our algorithm in comparison with other systems proves the efficacy of the system with a noticeable increase in detection ratios.

Image classification by addition of spatial information based on histograms of orthogonal vectors

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy.

MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is an open research problem. In the service of multimedia service, the requirement of Multimedia Indexing Technology is increasing to retrieve and search for interesting data from huge Internet. Since the traditional retrieval method, which is using textual index, has limitation to handle the multimedia data in current Internet, alternatively, the more efficient representation method is needed. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visual appearance. The color, shape, and texture are the examples of low-level image features. The feature combination that is also known as feature fusion is applied in CBIR to increase the performance, a single feature is not robust to the transformations that are in the image datasets. This paper represents a new Content-Based Image Retrieval (CBIR) technique to fuse the color and texture features to extract local features as our feature vector. The features are created for each image and stored as a feature vector in the database. The proposed research is divided into three phases that feature extraction, similarities match, and performance evaluation. Color Moments (CM) are used for Color features and extract the Texture features, used Gabor Wavelet and Discrete Wavelet transform. To enhance the power of feature vector representation, Color and Edge Directivity Descriptor (CEDD) is also included in the feature vector. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Corel-1500, and Ground Truth (GT) images dataset. The average precision and recall measures are used to evaluate the performance of the proposed research. The proposed approach is efficient in term of feature extraction and the efficiency and effectiveness of the proposed research outperform the existing research in term of average precision and recall values.

A review of data security and cryptographic techniques in IoT based devices

The idea of the Internet of Things (IoT) is to connect or give access to everything to the Internet. IoT environment not only provides the facility of Human to Machine connectivity, however, it also creates Machine to Machine connectivity. As everything is going to connect to the Internet and also generating the data. So, the data generating by these devices is growing up rapidly, that huge amount of data is called Big Data. This data has huge Volume, High Velocity, and different Variety. The security of this data is a risk. As we know that, the IoT devices have constraints like low power and less computational speed and the traditional encryption algorithms like DES, 3DES, and AES are more complex. Traditional encryption algorithm seems not feasible for IoT devices. So, we need to develop Lightweight encryption algorithm for IoT devices for secure communication and secure data transmission in IoT environment. Cryptography and Steganography techniques are used for securing the data over the Internet. Cryptography encrypts the data by using a key and make a ciphertext that cannot be readable by the normal user. Steganography hides the data by concealing it into another medium like data, image, audio, video, or mixed. This paper provides a review of important lightweight cryptographic techniques used for IoT devices.

Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.

Loss Based Congestion Control Module for Health Centers Deployed by Using Advanced IoT Based SDN Communication Networks

Many healthcare centers are deploying advanced Internet of Things (IoT) based on Software-Defined Networks (SDNs). Transmission Control Protocol (TCP) was developed to control the data transmission in wide range of networks and provides reliable communication by using many caching and congestion control schemes. TCP is predestined to always increase and decrease its congestion window size to make changes in traffic. Nowadays, about 50% IoT based SDN traffic is controlled by TCP CUBIC, which is the default congestion control scheme in Linux operating system. The aim of this research is to develop a new content-caching based congestion control scheme for advanced IoT enabled SDN networks to achieve better performance in healthcare infrastructure network environments. In this research, Congestion Control Module for Loss Event (CCM-LE) is proposed to enhance the performance of TCP CUBIC in advanced IoT based on SDN. Network Simulator 2 (NS-2) is used to simulate the experiments of CCM-LE and state-of-the-art schemes. Results show that the performance of CCM-LE outperforms by 19% as compared to state-of-the-art schemes.