IEEE 2016 / 17 - Image Processing Projects

IEEE 2016 :   PassBYOP: Bring Your Own Picture for Securing Graphical Passwords
IEEE 2016 Image Processing
Abstract:PassBYOP is a new graphical password scheme for public terminals that replaces the static digital images typically used in graphical password systems with personalized physical tokens, herein in the form of digital pictures displayed on a physical user-owned device such as a mobile phone. Users present these images to a system camera and then enter their password as a sequence of selections on live video of the token. Highly distinctive optical features are extracted from these selections and used as the password.We present three feasibility studies of PassBYOP examining its reliability, usability, and security against observation. The reliability study shows that image-feature based passwords are viable and suggests appropriate system thresholds—password items should contain a minimum of seven features, 40% of which must geometrically match originals stored on an authentication server in order to be judged equivalent. The usability study measures task completion times and error rates, revealing these to be 7.5 s and 9%, broadly comparable with prior graphical password systems that use static digital images. Finally, the security study highlights PassBYOP’s resistance to observation attack—three attackers are unable to compromise a password using shoulder surfing, camera based observation, or malware. These results indicate that Pass- BYOP shows promise for security while maintaining the usabilityof current graphical password schemes.

IEEE 2016 :   Single-sample Face Recognition Based on LPP Feature Transfer
IEEE 2016 Image Processing

Abstract:Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on Locality Preserving Projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Secondly, we project the vectors of source faces and target faces into feature sub-space by LPP respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL and Yale demonstrate the superiority of the proposed LPP feature transfer based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms such as (PC)2A and Block FLDA.

IEEE 2016 :   A Shoulder Surfing Resistant Graphical Authentication System
IEEE 2016 Image Processing

Abstract:Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. With a one-time valid login indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the password even they conduct multiple camera-based attacks. We also implemented a PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system achieves better resistance to shoulder surfing attacks while maintaining usability.

IEEE 2016 :   Reversible Data Hiding in Encrypted Images Based on Progressive Recovery
IEEE 2016 Image Processing

Abstract:This paper proposes a method of reversible data hiding in encrypted images (RDH-EI) based on progressive recovery. Three parties are involved in the framework, including the content owner, the data-hider, and the recipient. The content owner encrypts the original image using a stream cipher algorithm and uploads ciphertext to the server. The data-hider on the server divides the encrypted image into three channels and respectively embeds different amount of additional bits into each one to generate a marked encrypted image. On the recipient side, additional message can be extracted from the marked encrypted image, and the original image can be recovered without any errors. While most of the traditional methods use one criterion to recover the whole image, we propose to do the recovery by a progressive mechanism. Rate-distortion of the proposed method outperforms state-of-the-art RDH-EI methods.

IEEE 2016 :  Reversible Data Hiding in Encrypted Image with Distributed Source Encoding
IEEE 2016 Image Processing

 Abstract:— This paper proposes a novel scheme of reversible data hiding (RDH) in encrypted images using distributed source coding (DSC). After the original image is encrypted by the content owner using a stream cipher, the data-hider compresses a series of selected bits taken from the encrypted image to make room for the secret data. The selected bit series is Slepian-Wolf encoded using low density parity check (LDPC) codes. On the receiver side, the secret bits can be extracted if the image receiver has the embedding key only. In case the receiver has the encryption key only, he/she can recover the original image approximately with high quality using an image estimation algorithm. If the receiver has both the embedding and encryption keys, he/she can extract the secret data and perfectly recover the original image using the distributed source decoding. The proposed method outperforms previously published ones.

IEEE 2015 : An Attribute-assisted Reranking Model for Web Image Search
Abstract :Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. In this work, we propose a visual-attribute joint hypergraph learning approach to simultaneously explore two information sources. A hypergraph is constructed to model the relationship of all images. We conduct experiments on more than 1,000 queries in MSRA-MM V2.0 dataset. The experimental results demonstrate the effectiveness of our approach.

IEEE 2015 : Joint Feature Learning for Face Recognition

Abstract : This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. Since different face regions have different physical characteristics, we propose to use different feature dictionaries to represent them, and to learn multiple yet related feature projection matrices for these regions simultaneously. Hence position-specific discriminative information can be exploited for face representation. Having learned these feature projections for different face regions, we perform spatial pooling for face patches within each region to enhance the representative power of the learned features. Moreover, we stack our JFL model into a deep architecture to exploit hierarchical information for feature representation and further improve the recognition performance. Experimental results on five widely used face data sets show the effectiveness of our proposed approach.

IEEE 2015 : Learning to Rank Image Tags With Limited Training Examples

Abstract : With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.

IEEE 2015 : Data Hiding in Still Images Based on Blind Algorithm of Steganography

Abstract : Steganography is the science of hiding secret information in another unsuspicious data. Generally, a steganographic secret message could be a widely useful multimedia: as a picture, an audio file, a video file or a message in clear text - the covertext. The most recent steganography techniques tend to hide a secret message in digital images. We propose and analyze experimentally a blind steganography method based on specific attributes of two dimensional discrete wavelet transform set by Haar mother wavelet. The blind steganography methods do not require an original image in the process of extraction what helps to keep a secret communication undetected to third party user or steganalysis tools. The secret message is encoded by Huffman code in order to achieve a better imperceptibility result. Moreover, this modification also increases the security of the hidden communication.

IEEE 2015 : Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset

Abstract :   This paper introduces a method for face recognition across age and also a dataset containing variations of age in the wild. We use a data-driven method to address the cross-age face recognition problem, called cross-age reference coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC can encode the low-level feature of a face image with an age-invariant reference space. In the retrieval phase, our method only requires a linear projection to encode the feature and thus it is highly scalable. To evaluate our method, we introduce a large-scale dataset called cross-age celebrity dataset (CACD). The dataset contains more than 160 000 images of 2,000 celebrities with age ranging from 16 to 62. Experimental results show that our method can achieve state-of-the-art performance on both CACD and the other widely used dataset for face recognition across age. To understand the difficulties of face recognition across age, we further construct a verification subset from the CACD called CACD-VS and conduct human evaluation using Amazon Mechanical Turk. CACD-VS contains 2,000 positive pairs and 2,000 negative pairs and is carefully annotated by checking both the associated image and web contents. Our experiments show that although state-of-the-art methods can achieve competitive performance compared to average human performance, majority votes of several humans can achieve much higher performance on this task. The gap between machine and human would imply possible directions for further improvement of cross-age face recognition in the future.

IEEE 2015 : Steganography Using Reversible Texture Synthesis

Abstract :  We propose a novel approach for steganography using a reversible texture synthesis. A texture synthesis process resamples a smaller texture image, which synthesizes a new texture image with a similar local appearance and an arbitrary size. We weave the texture synthesis process into steganography to conceal secret messages. In contrast to using an existing cover image to hide messages, our algorithm conceals the source texture image and embeds secret messages through the process of texture synthesis. This allows us to extract the secret messages and source texture from a stego synthetic texture. Our approach offers three distinct advantages. First, our scheme offers the embedding capacity that is proportional to the size of the stego texture image. Second, a steganalytic algorithm is not likely to defeat our steganographic approach. Third, the reversible capability inherited from our scheme provides functionality, which allows recovery of the source texture. Experimental results have verified that our proposed algorithm can provide various numbers of embedding capacities, produce a visually plausible texture images, and recover the source texture.

IEEE 2015 : RRW—A Robust and Reversible Watermarking Technique for Relational Data

 Abstract : Advancement in information technology is playing an increasing role in the use of information systems comprising relational databases. These databases are used effectively in collaborative environments for information extraction; consequently, they are vulnerable to security threats concerning ownership rights and data tampering. Watermarking is advocated to enforce ownership rights over shared relational data and for providing a means for tackling data tampering. When ownership rights are enforced using watermarking, the underlying data undergoes certain modifications; as a result of which, the data quality gets compromised. Reversible watermarking is employed to ensure data quality along-with data recovery. However, such techniques are usually not robust against malicious attacks and do not provide any mechanism to selectively watermark a particular attribute by taking into account its role in knowledge discovery. Therefore, reversible watermarking is required that ensures; (i) watermark encoding and decoding by accounting for the role of all the features in knowledge discovery; and, (ii) original data recovery in the presence of active malicious attacks. In this paper, a robust and semi-blind reversible watermarking (RRW) technique for numerical relational data has been proposed that addresses the above objectives. Experimental studies prove the effectiveness of RRW against malicious attacks and show that the proposed technique outperforms existing ones.

IEEE 2015 : Query Aware Determinization of Uncertain Objects

Abstract : The determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment techniques such as entity resolution, information extraction, and speech processing. The legacy system may correspond to pre-existing web applications such as Flickr, Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the quality of the end-application built on deterministic data. We explore such a determinization problem in the context of two different data processing tasks—triggers and selection queries. We show that approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets.

IEEE 2015 : Automatic Face Naming by Learning Discriminative  Affinity Matrices From Weakly Labeled Images

Abstract :  Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

IEEE 2014 : FeatureMatch: A General ANNF Estimation Technique and its Applications

Abstract : In this paper, we propose FeatureMatch, a generalised approximate nearest-neighbour field (ANNF) computation framework, between a source and target image. The proposed algorithm can estimate ANNF maps between any image pairs, not necessarily related. This generalisation is achieved through appropriate spatial-range transforms. To compute ANNF maps, global colour adaptation is applied as a range transform on the source image. Image patches from the pair of images are approximated using low-dimensional features, which are used along with KD-tree to estimate the ANNF map. This ANNF map is further improved based on image coherency and spatial transforms. The proposed generalisation, enables us to handle a wider range of vision applications, which have not been tackled using the ANNF framework. We illustrate two such applications namely: 1) optic disk detection and 2) super resolution. The first application deals with medical imaging, where we locate optic disks in retinal images using a healthy optic disk image as common target image. The second application deals with super resolution of synthetic images using a common source image as dictionary. We make use of ANNF mappings in both these applications and show experimentally that our proposed approaches are faster and accurate, compared with the state-of the- art techniques.

IEEE 2014 : Web Image Re-Ranking Using Query-Specific Semantic Signatures

Abstract :  Image re-ranking, as an effective way to improve the results of web-based image search, has been adopted by current commercial search engines. Given a query keyword, a pool of images is first retrieved by the search engine based on textual information. By asking the user to select a query image from the pool, the remaining images are re-ranked based on their visual similarities with the query image. A major challenge is that the similarities of visual features do not well correlate with images’ semantic meanings which interpret users’ search intention. On the other hand, learning a universal visual semantic space to characterize highly diverse images from the web is difficult and inefficient. In this paper, we propose a novel image re-ranking framework, which automatically offline learns different visual semantic spaces for different query keywords through keyword expansions. The visual features of images are projected into their related visual semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the visual semantic space specified by the query keyword. The new approach significantly improves both the accuracy and efficiency of image re-ranking. The original visual features of thousands of dimensions can be projected to the semantic signatures as short as 25 dimensions. Experimental results show that 20% 􀀀 35% relative improvement has been achieved on re-ranking precisions compared with the state of- the-art methods.

IEEE 2014: Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems

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.

IEEE 2014: Online Payment System using Steganography and Visual Cryptography

Abstract :  A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.

IEEE 2014: Semi-Supervised Biased Maximum Margin Analysis for Interactive Image Retrieval

 Abstract :  With many potential practical applications, Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years. A variety of Relevance Feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts and thus to improve the performance of CBIR systems. Among various RF approaches, Support Vector Machine (SVM) based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as a RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM based RF techniques do not take into account the unlabelled samples although they are very helpful in constructing a good classifier. To explore solutions to overcome these two drawbacks, in this work, we propose a Biased Maximum Margin Analysis (BMMA) and a Semi-Supervised Biased Maximum Analysis (Semi BMMA), for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, while the Semi BMMA can effectively integrate information of unlabelled samples by introducing a Laplacian regularize to the BMMA. We formulate the problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems.

IEEE 2014 : An Access Point-Based FEC Mechanism for Video Transmission over Wireless LANs

Abstract :  Forward Error Correction (FEC) is one of the most common means of performing packet error recovery in data transmissions. FEC schemes typically tune the FEC rate in accordance with feedback information provided by the receiver. However, the feedback and FEC rate calculation processes inevitably have a finite duration, and thus the FEC rate implemented at the sender may not accurately reflect the current state of the network. Thus, this paper proposes an Enhanced Random Early Detection Forward Error Correction (ERED-FEC) mechanism to improve the quality of video transmissions over Wireless Local Area Networks (WLANs). In contrast to most FEC schemes, the FEC redundancy rate is calculated directly at the Access Point (AP). Moreover, the redundancy rate is tuned in accordance with both the wireless channel condition (as indicated by the number of packet retransmissions) and the network traffic load (as indicated by the AP queue length). The experimental results show that the proposed ERED-FEC mechanism achieves a significant improvement in the video quality compared to existing FEC schemes without introducing an excessive number of redundant packets into the network.

IEEE 2013 : A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization

Abstract :  Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ??semantic gap?? Problem. In this paper, an innovative approach is proposed that combines a relevance feedback (RF) approach with an evolutionary stochastic algorithm, called particle swarm optimizer (PSO), as a way to grasp user's semantics through optimized iterative learning. The retrieval uses human interaction to achieve a twofold goal: 1) to guide the swarm particles in the exploration of the solution space towards the cluster of relevant images; 2) to dynamically modify the feature space by appropriately weighting the descriptive features according to the users' perception of relevance. Extensive simulations showed that the proposed technique outperforms traditional deterministic RF approaches of the same class, thanks to its stochastic nature, which allows a better exploration of complex, nonlinear and highly-dimensional solution spaces.

IEEE 2013 : A multi-resolution image fusion scheme for 2D images based on wavelet transform

Abstract :  The fusion of images is the process of combining two or more images into a single image retaining important features from each of the images. A scheme for fusion of multi-resolution 2D gray level images based on wavelet transform is presented in this paper. If the images are not already registered, a point-based registration, using affine transformation is performed prior to fusion. The images to be fused are first decomposed into sub images with different frequency and then information fusion is performed using these images under the proposed gradient and relative smoothness criterion. Finally these sub images are reconstructed into the result image with plentiful information. A quantitative measure of the degree of fusion is estimated by cross-correlation coefficient and comparison with some of the existing wavelet transform based image fusion techniques is carried out.

IEEE 2013 : A Proposal to Prevent Click-Fraud Using Clickable CAPTCHAs 
Abstract :  Advertising on the Internet is a key factor for the success of several businesses nowadays. The Internet has evolved to a point where it has become possible to develop a business model completely based on Web advertising, which is important for the consolidation of such a model and the continuity of the Internet itself. However, it is often observed that some content publishers are dishonest and employ automated tools to generate traffic and profit by defrauding advertisers. Similarly, some advertisers use automated tools to click on the ads of their competitors, aiming to exhaust the budget of the competitor's marketing departments. In this paper, differently of recent click fraud detection mechanisms, that take place after the fraud has already occurred, we propose an approach for preventing automated click-fraud, based on the use of click able CAPTCHAs.

IEEE 2013 : Intent Search: Capturing User Intention for One-Click Internet Image Search 

Abstract :  Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved by text-based search are re ranked based on both visual and textual content. Our key contribution is to capture the users' search intention from this one-click query image in four steps. 1) The query image is categorized into one of the predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a specific weight schema is used to combine visual features adaptive to this kind of image to better re rank the text-based search result. 2) Based on the visual content of the query image selected by the user and through image clustering, query keywords are expanded to capture user intention. 3) Expanded keywords are used to enlarge the image pool to contain more relevant images. 4) Expanded keywords are also used to expand the query image to multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to further improve content-based image re ranking. All these steps are automatic, without extra effort from the user. This is critically important for any commercial web-based image search engine, where the user interface has to be extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in Internet image search are designed. Experimental evaluation shows that our approach significantly improves the precision of top-ranked images and also the user experience.

IEEE 2013 : Visual cryptography Scheme for color Image Using Random number - eStamp Authentication

Abstract :   Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human visual system. The benefit of the visual secret sharing scheme is in its decryption process where without any complex cryptographic computation encrypted data is decrypted using Human Visual System (HVS). But the encryption technique needs cryptographic computation to divide the image into a number of parts let n. k-n secret sharing scheme is a special type of Visual Cryptographic technique where at least a group of k shares out of n shares reveals the secret information, less of it will reveal no information. In our paper we have proposed a new k-n secret sharing scheme for color image where encryption (Division) of the image is done using Random Number generator.

IEEE 2013 : Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

Abstract :  This paper proposes a complete practical methodology for minimizing additive distortion in Steganography with general (no binary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the no binary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolution codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in Steganography applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in Steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.

IEEE 2014 : 3D Facial Land marking under Expression, Pose, and Occlusion Variations

Abstract :  Automatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose variations and occlusions also hamper accurate detection of landmarks. In this paper we assess a fully automatic 3D facial land marking algorithm that relies on accurate statistical modeling of facial features. This algorithm can be employed to model any facial landmark, provided that the facial poses present in the training and test conditions are similar. We test this algorithm on the recently acquired Bosporus 3D face database, and also inspect cross-database performance by using the FRGC database. Then, a curvature-based method for localizing the nose tip is introduced and shown to perform well under severe conditions.

IEEE 2013 : Novel Approach for Color Extended Visual Cryptography Using Error Diffusion

Abstract :   Visual cryptography, an emerging cryptography technology, uses the characteristics of human vision to decrypt encrypted images. Cryptography is the study of mathematical techniques related aspects of Information Security such as confidentiality, data security, entity authentication and data origin authentication, but it is not the only means of providing information security, rather one of the techniques. Visual cryptography is a new technique which provides information security which uses simple algorithm unlike the complex, computationally intensive algorithms used in other techniques like traditional cryptography.  Previous methods in the literature show good results for black and white or gray scale VC schemes, however, they are not sufficient to be applied directly to color shares due to different color structures. Some methods for color visual cryptography are not satisfactory in terms of producing either meaningless shares or meaningful shares with low visual quality, leading to suspicion of encryption. Color visual cryptography (VC) encrypts a color secret message into color halftone image shares. This project introduces the concept of visual information pixel (VIP) synchronization and error diffusion to attain a color visual cryptography encryption method that produces meaningful color shares with high visual quality.  VIP synchronization retains the positions of pixels carrying visual information of  Original images throughout the color channels and error diffusion generates shares pleasant to human eyes. Comparisons with previous approaches show the superior performance of the new method.

IEEE 2013 : A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data
IEEE  2013  Transactions on Knowledge and Data Engineering
Abstract :  Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm, FAST, is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent, the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers, namely, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection. The results, on 35 publicly available real-world high dimensional image, microarray, and text data, demonstrate that FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers.

IEEE 2013: Property Analysis of XOR Based Visual  Cryptography
IEEE 2013 Transactions on Communication Systems and Network Technologies
Abstract : A (k,  n) Visual Cryptographic Scheme (VCS) encodes a secret image into n shadow images (printed on Transparencies) distributed among n participants. When any k participants superimpose their transparencies on an overhead projector (OR operation), the secret image can be visually revealed by human visual system without computation. However, the monotone property of OR operation degrades the visual quality of reconstructed image for OR-based VCS (OVCS).  Accordingly, XOR-based VCS (XVCS), which uses XOR operation for decoding, was proposed to enhance the contrast. In this paper, we investigate the relation between OVCS and XVCS. Our main contribution is to theoretically prove that the basis matrices of (k, n)-OVCS can be used in (k, n)-XVCS. Meantime, the contrast is enhanced 2 1) times.-(k

IEEE 2013: Securing Visual Cryptographic Shares using Public Key Encryption
IEEE 2013 Transactions on Communication Systems and Network Technologies

Abstract :  The Visual Cryptography Scheme is a secure method that encrypts a secret document or image by breaking it into shares. A distinctive property of Visual Cryptography Scheme is that one can visually decode the secret image by superimposing shares without computation. By taking the advantage of this property, third person can easily retrieve the secret image if shares are passing in sequence over the network. The project presents an approach for encrypting visual cryptographically generated image shares using Public Key Encryption. RSA algorithm is used for providing the double security of secret document. Thus secret share are not available in their actual form for any alteration by the adversaries who try to create fake shares. The scheme provides more secure secret shares that are robust against a number of attacks & the system provides a strong security for the handwritten text, images and printed documents over the public network. 

IEEE 2013: Super-Resolution-based In painting
IEEE 2013 Transactions on Image Processing

Abstract :  This paper introduces a new exemplar-based in painting frame-work. A coarse version of the input image is first in painted by a non-parametric patch sampling. Compared to existing approaches, some improvements have been done (e.g. filling order computation, combination of K nearest neighbor). The in painted of a coarse version of the input image allows to reduce the computational complexity, to be less sensitive to noise and to work with the dominant orientations of image structures. From the low-resolution in painted image, a single-image super-resolution is applied to recover the details of missing areas. Experimental results on natural images and texture synthesis demonstrate the effectiveness of the proposed method.

IEEE 2013: Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption
IEEE 2013 Transactions on Information Forensics and Security

Abstract :  Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content’s confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods.

IEEE 2013: EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval
IEEE 2013 Transactions on Knowledge and Data Engineering

Abstract : Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model - the Ranking on Data Mani fold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications.

IEEE 2013: Steganography using Genetic Algorithm along with Visual Cryptography for Wireless Network Application.
IEEE 2013 Transactions on Communication Systems and Network Technologies
Abstract :  Image Stenography is an emerging field of research for secure data hiding and transmission over networks. The proposed system provides the best approach for Least Significant Bit (LSB) based Stenography using Genetic Algorithm (GA) along with Visual Cryptography (VC). Original message is converted into cipher text by using secret key and then hidden into the LSB of original image. Genetic Algorithm and Visual Cryptography has been used for enhancing the security. Genetic Algorithm is used to modify the pixel location of stego image and the detection of this message is complex. Visual Cryptography is used to encrypt the visual information. It is achieved by breaking the image into two shares based on a threshold. The performance of the proposed system is experimented by performing steganalysis and conducting benchmarking test for analysing the parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The main aim of this paper is to design the enhanced secure algorithm which uses both steganography using Genetic Algorithm and Visual Cryptography to ensure improved security and reliability.

IEEE 2013: Scalable Face Image Retrieval using Attribute-Enhanced Sparse Code words
IEEE 2013 Transactions on Multimedia 
Abstract :  Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve content-based face retrieval by constructing semantic code words for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attribute-embedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public data sets, the results show that the proposed methods can achieve up to 43.5% relative improvement in MAP compared to the existing method.

IEEE 2013 Transactions on Communication Systems and Network Technologies

Abstract :  Visual cryptography is a secret sharing scheme which uses images distributed as shares such that, when the shares are superimposed, a hidden secret image is revealed. In extended visual cryptography, the share images are constructed to contain meaningful cover images, thereby providing opportunities for integrating visual cryptography and biometric security techniques. In this paper, we propose a method for processing halftone images that improves the quality of the share images and the recovered secret image in an extended visual cryptography scheme for which the size of the share images and there covered image is the same as for the original halftone secret image. The resulting scheme maintains the perfect security of the original extended visual cryptography approach

IEEE 2013: An Encryption and Decryption Algorithm for Image Based on DNA
IEEE 2013 Transactions on Communication Systems and Network Technologies

Abstract :  A novel image encryption algorithm based on DNA sequence addition operation. This initiation and increasing escalation of Internet has caused the information to be paperless and the makeover into electronic compared to the conventional digital image distribution. In this paper we proposed and implement four phase. First phase, image is renovating into binary matrix. Afterward matrix is apportioning into equal blocks. Second phase, each block is then encoded into DNA sequences and DNA sequence addition operation used to add these blocks. For that result of added matrix is achieved by using two Logistic maps. At the time of decoding the DNA sequence matrix is complemented and we encrypt that result by using DES then we get encrypted image. Our paper includes a novel encryption technique for providing security to image. We have proposed an algorithm which is based on suitable encryption method.

IEEE 2013: Combined texture  and Shape  Features  for Content Based Image  Retrieval
IEEE 2013 Transactions on Power and Computing Technologies

Abstract :  Image  retrieval  refers to  extracting  desired  images from  a  large  database.  The  retrieval  may  be  of  text  based  or content  based.  Here  content  based  image  retrieval (CBIR) is performed.  CBIR is a long standing  research  topic  in  the  field of multimedia.  Here features  such  as  texture  &  shape  are  analyzed. Gabor filter is used to extract texture  features  from  images. Morphological c10sing  operation combined with Gabor  filter gives  better  retrieval  accuracy.  The  parameters  considered  are scale  and  orientation.  After  applying Gabor filter  on  the image, texture  features  such  as  mean  and standard  deviations  are calculated. This forms  the  feature  vector.  Shape  feature  is extracted  by  using  Fourier  Descriptor  and  the  centroid  distance. In order to  improve  the  retrieval  performance,  combined texture and  shape  features  are  utilized,  because  many  features  provide more  information  than  the  single  feature.  The  images are extracted  based  on  their Euclidean  distance.  The  performance  is evaluated  using precision-recall  graph. 

IEEE 2013: Beyond Text QA: Multimedia Answer Generation by Harvesting Web Information
IEEE 2013 Transactions on multimedia

Abstract :  Community question answering (cQA) services have gained popularity over the past years. It not only allows community members to post and answer questions but also enables general users to seek information from a comprehensive set of well-answered questions. However, existing cQA forums usually provide only textual answers, which are not informative enough for many questions. In this paper, we propose a scheme that is able to en-rich textual answers in cQA with appropriate media data. Our scheme consists of three components: answer medium selection, query generation for multimedia search, and multimedia data selection and presentation. This approach automatically determines which type of media information should be added for a textual answer. It then automatically collects data from the web to enrich the answer. By processing a large set of QA pairs and adding them to a pool, our approach can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. Different from a lot of MMQA research efforts that attempt to directly answer questions with image and video data, our approach is built based on community-contributed textual answers and thus it is able to deal with more complex questions. We have conducted extensive experiments on a multi source QA data set. The results demonstrate the effectiveness of our approach.


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