Zhihua Xia

A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF x IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

Steganalysis of LSB matching using differences between nonadjacent pixels

This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels. Using the characteristic function of difference histogram (DHCF), we prove that the center of mass of DHCF (DHCF COM) decreases after messages are embedded. Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances. The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier. The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well.

A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing

With the increasing importance of images in peoples daily life, content-based image retrieval (CBIR) has been widely studied. Compared with text documents, images consume much more storage space. Hence, its maintenance is considered to be a typical example for cloud storage outsourcing. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which makes the CBIR technologies in plaintext domain to be unusable. In this paper, we propose a scheme that supports CBIR over encrypted images without leaking the sensitive information to the cloud server. First, feature vectors are extracted to represent the corresponding images. After that, the pre-filter tables are constructed by locality-sensitive hashing to increase search efficiency. Moreover, the feature vectors are protected by the secure kNN algorithm, and image pixels are encrypted by a standard stream cipher. In addition, considering the case that the authorized query users may illegally copy and distribute the retrieved images to someone unauthorized, we propose a watermark-based protocol to deter such illegal distributions. In our watermark-based protocol, a unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user. Hence, when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction. The security analysis and the experiments show the security and efficiency of the proposed scheme.

Towards Privacy-Preserving Content-Based Image Retrieval in Cloud Computing

Content-based image retrieval (CBIR) applications have been rapidly developed along with the increase in the quantity, availability and importance of images in our daily life. However, the wide deployment of CBIR scheme has been limited by its the severe computation and storage requirement. In this paper, we propose a privacy-preserving content-based image retrieval scheme, which allows the data owner to outsource the image database and CBIR service to the cloud, without revealing the actual content of the database to the cloud server. Local features are utilized to represent the images, and earth movers distance (EMD) is employed to evaluate the similarity of images. The EMD computation is essentially a linear programming (LP) problem. The proposed scheme transforms the EMD problem in such a way that the cloud server can solve it without learning the sensitive information. In addition, local sensitive hash (LSH) is utilized to improve the search efficiency. The security analysis and experiments show the security and efficiency of the proposed scheme.

EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing

Abstract The content-based image retrieval (CBIR) has been widely studied along with the increasing importance of images in our daily life. Compared with the text documents, images consume much more storage and thus are very suitable to be stored on the cloud servers. The outsourcing of CBIR to the cloud servers can be a very typical service in cloud computing. For the privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before being outsourced, which will cause the CBIR technologies in plaintext domain unusable. In this paper, we propose a scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server. Firstly, the feature vectors are extracted to represent the corresponding images. Then, the pre-filter tables are constructed with the locality-sensitive hashing to increase the search efficiency. Next, the feature vectors are protected by the secure k-nearest neighbor (kNN) algorithm. The security analysis and experiments show the security and efficiency of the proposed scheme.

Secure semantic expansion based search over encrypted cloud data supporting similarity ranking

With the advent of cloud computing, more and more information data are outsourced to the public cloud for economic savings and ease of access. However, the privacy information has to be encrypted to guarantee the security. To implement efficient data utilization, search over encrypted cloud data has been a great challenge. The existing solutions depended entirely on the submitted query keyword and didn’t consider the semantics of keyword. Thus the search schemes are not intelligent and also omit some semantically related documents. In view of the deficiency, as an attempt, we propose a semantic expansion based similar search solution over encrypted cloud data. Our solution could return not only the exactly matched files, but also the files including the terms semantically related to the query keyword. In the proposed scheme, a corresponding file metadata is constructed for each file. Then both the encrypted metadata set and file collection are uploaded to the cloud server. With the metadata set, the cloud server builds the inverted index and constructs semantic relationship library (SRL) for the keywords set. After receiving a query request, the cloud server first finds out the keywords that are semantically related to the query keyword according to SRL. Then both the query keyword and the extensional words are used to retrieve the files. The result files are returned in order according to the total relevance score. Eventually, detailed security analysis shows that our solution is privacy-preserving and secure under the previous searchable symmetric encryption (SSE) security definition. Experimental evaluation demonstrates the efficiency and effectives of the scheme.

A copy-move forgery detection method based on CMFD-SIFT

A very common way of image tampering is the copy-move attack. When creating a copy-move forgery, it is often necessary to add or remove important objects from an image. To carry out forensic analysis of such images, various copy-move forgery detection (CMFD) methods have been developed in the literatures. In recent years, many feature-based CMFD approaches have emerged due to its excellent robustness to various transformations. However there is still place to improve performance further. Many of them would suffer from the problem of insufficient matched key-points while performing on the mirror transformed forgeries. Furthermore, many feature-based methods might hardly expose the tempering when the forged region is of uniform texture. In this paper, a novel feature-based CMFD method is proposed. Key-points are detected by using a modified SIFT-based detector. A novel key-points distribution strategy is developed for interspersing the key-points evenly throughout an image. Finally, key-points are descripted by an improved SIFT descriptor which is enhanced for the CMFD scenario. Extensive experimental results are presented to confirm the efficacy.

Steganalysis of two least significant bits embedding based on least square method

The purpose of steganalysis is to detect the existence of the hidden messages. In this paper, a steganalysis algorithm against two least significant bits steganography is proposed in order to estimate the hidden message length in images. In the method, firstly we estimate an approximately original image through a local masked estimation function. Then we construct a weighted stego image by extending the definition which was proposed in LSB steganalysis to 2LSB. Finally, detection equation is formulated as a simple optimization problem between approximately original image and weighted stego image. Hidden message length can be easily calculated by solving the detection equation. Experimental results reveal that this algorithm can estimate the length of secret message with high accuracy.

JPEG image steganalysis using joint discrete cosine transform domain features

A JPEG image steganalysis scheme based on joint dis- crete cosine transform (DCT) domain features is proposed. Intrinsic characteristics of DCT coefficients, such as histogram, intrablock correlation, and interblock correlation, are exploited to construct three feature sets. Support vector machine is utilized to learn and discriminate the difference of features between cover and stego im- ages. First, the three feature sets are investigated separately to re- veal their individual capability of attacking steganographic methods. Second, the feature sets are combined to form a joint feature set with better performance. Experimental results demonstrate that all three feature sets individually succeed in attacking the four typical steganographic tools to some extent, with the intrablock feature set performing the best. Furthermore, the comparison experiments show that the jointed feature set not only outperforms the three in- dividual feature sets but also proves to be better than a previous state-of-the-art steganalysis method.

Fingerprint liveness detection using multiscale difference co-occurrence matrix

Abstract. Fingerprint identification systems have been widely applied in both civilian and governmental applications due to its satisfying performance. However, the fingerprint identification systems can be easily cheated by the presentation of artificial fingerprints made from common materials. Therefore, it reduces the reliability and misleads the decision of the fingerprint identification systems. In this work, we propose a software-based fingerprint liveness detection method based on multiscale difference co-occurrence matrix (DCM). In doing so, multiscale wavelet transform operation is first conducted on the original image. After the preprocessing of the decomposition of the original image, DCMs are computed by using the Laplacian operator. Horizontal and vertical difference co-occurrence matrices are constructed in our method. In order to reduce the dimensionality of the feature vectors, truncation operation is introduced for DCMs. Then, the elements of processing DCMs are regarded as the texture features of original fingerprint images. Finally, classification accuracy of feature vectors is predicted based on a support vector machine classifier. The experimental results have shown that the performance of our method is very promising and meanwhile achieve better accurate classification compared with the best algorithms of LivDet2013 and LivDet2011, while being able to recognize spoofed fingerprints with better recognition accuracy.