Abstract: Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the
literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality
assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their
Spearman correlation with sample utility. The authors’ evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.
Abstract—Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been underexplored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP
is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP
password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.
Abstract—Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved
Abstract—Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local
pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD)
and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy
compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVDbased methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms
in terms of both quantitative metrics and subjective visual quality.
Abstract : Steganography is an art to hide the existence of important information in a cover file. It is an information hiding technique which is used for sending and receiving confidential data over internet. Steganography is done in two part first is to
embed data in regular computer file and the second part to extract that information. Secret data can be embed in various regular computer file but video files plays an important role by providing more embedding space. This paper will provide a survey of various research papers on video stegnography.
Abstract : Object tracking is an important task within the field of computer vision. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the interesting need for automated video analysis has generated a great deal of interest in object tracking.In its simplest form, tracking can be defined as a method of following an object through successive image frames to determine its relative movement with respect to other objects. In other words, a tracker assigns consistent labels to the tracked objects in different frames of video.
One can simplify tracking by imposing constraints on the motion or appearance of objects. One can further constrain the object motion to be of constant velocity or acceleration based on prior information. Prior knowledge about the number and the size of objects, or the object appearance and shape can also be used to simplify the problem.
Abstract —We describe Google’s online handwriting recognition system that currently supports 22 scripts and 97 languages. The system’s focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and scripts. This made it possible to build recognizers for languages that, to the best of our knowledge, are not handled by any other online handwriting recognition system. The approach also enabled us to use the same architecture both on very powerful machines for recognition in the cloud as well as on mobile devices with more limited computational power by changing some of the settings of the system. In this paper we give a general overview of the system architecture and the novel components, such as unified time- and position-based input interpretation, trainable segmentation, minimum-error rate training for feature combination, and a
cascade of pruning strategies. We present experimental results for different setups. The system is currently publicly available in severalGoogle products, for example in Google Translate and as an input method for Android devices.
Abstract : As the amount of Web information grows rapidly, search engines must be able to retrieve information according to the user's preference. In this paper, we propose a new web search personalization approach that captures the user's interests and preferences in the form of concepts by mining search results and their clickthroughs. Due to the important role location information plays in mobile search, we separate concepts into content concepts and location concepts, and organize them into ontologies to create an ontology-based, multi-facet (OMF) prole to precisely capture the user's content and location interests and hence improve the search accuracy. Moreover, recognizing the fact that different users and queries may have different emphases on content and location information, we introduce the notion
of content and location entropies to measure the amount of content and location information associated with a query, and click content and location entropies to measure how much the user is interested in the content and location information in
the results. Accordingly, we propose to dene personalization effectiveness based on the entropies and use it to balance the weights between the content and location facets. Finally, based on the derived ontologies and personalization effectiveness, we train an SVM to adapt a personalized ranking function for re-ranking of future search. We conduct extensive experiments to compare the precision produced by our OMF proles and that of a baseline method. Experimental results show that OMF
improves the precision signicantly compared to the baseline.
Abstract — In this era due to unbelievable development in internet, various online attacks has been increased. From all such attacks most popular attack is phishing. This attacks are done for extracting confidential information such as banking information, passwords from unsuspecting victims for fraud purposes. Confidential data can’t be directly uploaded on website since it is risky. Here in this paper data is encrypted in video and visual cryptography for login purpose in our online database system for providing more security .
Abstract —This paper proposes a generic methodology for the semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability, is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. We present results on a data set of ∼6 h captured by 4 cameras, featuring a person in a holiday flat, performing activities such as walking, cooking, eating, cleaning, and watching TV. When aiming for a tracking accuracy of 60 cm, 80% of all video frames are automatically annotated. The annotations for the remaining 20% of the frames were added after human verification of an automatically selected subset of data. This involved ∼2.4 h of manual labor. According to a subsequent comprehensive visual inspection to judge the annotation procedure, we found 99% of the automatically annotated frames to be correct. We provide guidelines on how to apply the proposed
methodology to new data sets. We also provide an exploratory study for the multi-target case, applied on the existing and new benchmark video sequences.
Abstract — Secure and efficient data storage is needed in the cloud environment in modern era of information technology industry. In the present scenario the cloud verifies the authenticity of the cloud services without the knowledge of user’s identity. The cloud provides massive data access directly through the internet. Centralized storage mechanism is followed here for effective accessing of data. Cloud service providers are normally acquires the software and hardware resources and the cloud consumers are avail the services through the internet access in lease basis. Cloud security is enhanced through cryptography technique applied to the cloud security to avoid vulnerability. The intractable computability is achieved in the cloud by using the public key cryptosystem. This paper proposed the approach of applying Hyper elliptic curve cryptography for data protection in the cloud with the small key size. The proposed system has the further advantage of eliminating intruder in cloud computing. Efficacy of the system is to provide the high security of the cloud data.
Abstract : The content-based image retrieval (CBIR) is the most acceptable and often used image retrieval method, because it can be used to manage image database efficiently and effectively. The CBIR methods usually retrieve the images by image features. In this paper, we exploit a region called affine invariant region (AIR) as an image feature to help effectively retrieving the images which have been attacked or processed. Moreover, we use vector quantization to reduce the features comparison for improving the retrieval efficiency. The experimental results show that the method with high recall and precision is promising.
Abstract —In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on
increasingly wide-spread subjects in the field of RDH research that have been published these days. In this survey paper the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain, 2) RDH into image compressed domain (e.g., JPEG), 3) RDH
suitable for image semi-fragile authentication, 4) RDH with image contrast enhancement, 5) RDH into encrypted images, which is expected to have wide application in the cloud computation, and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.
Abstract —Over the last 25 years, there has been much work on multimedia digital watermarking. In this domain, the primary limitation to watermark strength has been in its visibility. For multimedia watermarks, invisibility is defined in human terms (that is, in terms of human sensory limitations). In this paper, we review recent developments in the non-media applications of data watermarking, which have emerged over the last decade as an exciting new sub-domain. Since by definition, the intended receiver should be able to detect the watermark, we have to redefine invisibility in an acceptable way that is often application-specific and thus cannot be easily generalized. In particular, this is true when the data is not intended to be directly consumed by humans. For example, a loose definition of robustness might be in terms of the resilience of a watermark against normal host data operations, and of invisibility as resilience of the data interpretation against change introduced by the watermark. In our paper, we classify the data in terms of data mining rules on complex types of data such as time-series, symbolic sequences, data streams and so forth. We emphasize the challenges involved in non-media watermarking in terms of common watermarking properties including invisibility, capacity, robustness, and security. With the aid of a few examples of watermarking applications, we demonstrate these distinctions and we look at the latest research in this regard to make our argument clear and more meaningful. As the last aim, we look at the new challenges of digital watermarking that have arisen with the evolution of big data.
ABSTRACT In wireless communications, sensitive information is frequentlyexchanged, requiring remote authentication. Remote authentication involves the submission of encrypted information, along with visual and audio cues (facial images/videos, human voice, and so on). Nevertheless, Trojan horse and other attacks can cause serious problems, especially in the cases of remote examinations (in remote studying) or interviewing (for personnel hiring). This paper proposes a robust authentication mechanism based on semantic segmentation, chaotic encryption, and data hiding. Assuming that user X wants to be remotely
authenticated, initially X's video object (VO) is automatically segmented, using a head-and-body detector. Next, one of X's biometric signals is encrypted by a chaotic cipher. Afterwards, the encrypted signal is inserted to the most signicant wavelet coefcients of the VO, using its qualied signicant wavelet trees (QSWTs). QSWTs provide both invisibility and signicant resistance against lossy transmission and compression, conditions that are typical of wireless networks. Finally, the inverse discrete wavelet transform is applied to provide the stego-object. Experimental results regarding: 1) security merits of the proposed encryption scheme; 2) robustness to steganalytic attacks, to various transmission losses and JPEG compression ratios; and 3) bandwidth efciency measures indicate the promising performance of the proposed biometrics-based authentication scheme.
Visual target tracking is widely applied in visual surveillance,
human–computer interaction, visual navigation and activity analysis.
However, the response speed of conventional tracking systems is
limited to <60 fps due to serial processing. Some researchers adopt parallel
single-instruction-multiple-data (SIMD) processors to speed up
tracking algorithms [1–3]. However, these processors can only carry
out simple algorithms such as background subtraction, segmentation
and motion detection, thus they can only be applied to certain sceneries
with a clean background. The local binary pattern (LBP) histogram of
gradient (HOG) feature description is widely used in target detection
and tracking [4, 5]. However, both the HOG and LBP histograms are
rotation variant, which results in target shifting and tracking failure. In
this Letter, we propose a mixed rotation invariant description
(MRID)-based tracking algorithm and a novel high-speed visual tracking
system. This MRID is invariant to rotation and illumination changes so
that it achieves more robust tracking than previously reported fast tracking
algorithms. The proposed tracking system integrates processors with
pixel and row-level parallelism to speed up the tracking algorithm. The
system with hierarchical parallelism can achieve over 1000 fps processing
ABSTRACT | Despite rapid advances in the study of brain– computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major
barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brainmechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.
Abstract —Semantic image segmentation is a fundamental yet challenging problem, which can be viewed as an extension of the conventional object detection with close relation to image segmentation and classification. It aims to partition images into
non-overlapping regions that are assigned predefined semantic labels. Most of the existing approaches utilize and integrate lowlevel local features and high-level contextual cues, which are fed into an inference framework such as, the conditional random field (CRF). However, the lack of meaning in the primitives (i.e., pixels or superpixels) and the cues provides low discriminatory capabilities, since they are rarely object-consistent. Moreover, blind combinations of heterogeneous features and contextual cues exploitation through limited neighborhood relations in the CRFs tend to degrade the labeling performance. This paper proposes an ontology-based semantic image segmentation (OBSIS) approach that jointly models image segmentation and object detection. In particular, a Dirichlet process mixture model transforms the low-level visual space into an intermediate semantic space, which drastically reduces the feature dimensionality. These features are then individually weighed and independently learned within the context, using multiple CRFs. The segmentation of images into object parts is hence reduced to a classification task, where object inference is passed to an ontology model. This model resembles the way by which humans understand the images through the combination of different
cues, context models, and rule-based learning of the ontologies. Experimental evaluations using the MSRC-21 and PASCAL VOC’2010 data sets show promising results.
ABSTRACT : Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of signicance. The main goal of this paper was to summarize current
video and image defogging algorithms. We rst presented a review of the detection and classication method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied.
Abstract —Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the number of PWs in each stage. Moreover, it has to generate too many PWs in the initialization step and it unnecessarily regenerates too many PWs around object-like regions. In this paper, we attempt to solve the problems of MPW. An important fact we
used is that there is a large probability for a randomly generated PW not to contain the object because the object is a sparse event relative to the huge number of candidate windows. Therefore, we design a PD so as to efficiently reject the
huge number of nonobject windows. Specifically, we propose the concepts of rejection, acceptance, and ambiguity windows and regions. Then, the concepts are used to form and update a dented uniform distribution and a dented Gaussian distribution. This contrasts to MPW which utilizes only on region of support. The PD of MPW is acceptance-oriented whereas the PD of our method (called iPW) is rejection-oriented. Experimental results on human and face detection demonstrate the efficiency and the effectiveness of the iPW algorithm. The source code is publicly