In recent years, video surveillance has become essential for security applications used to monitor many organizations and locations, and it is therefore important to ensure the reliability of these surveillance videos. Unfortunately, surveillance videos can be forged with little effort by deleting an object from a video scene while leaving no visible traces. A fundamental challenge in video security is to determine whether or not an object has been removed from a video. This task is particularly challenging due to the lack of ground truth bases that can be used to verify the originality and integrity of video contents. In this paper, we propose a novel approach based on sequential and patch analyses to detect object removal forgery and to localize forged regions in videos. Sequential analysis is performed by modeling video sequences as stochastic processes, where changes in the parameters of these processes are used to detect a video forgery. Patch analysis is performed by modeling video sequences as a mixture model of normal and anomalous patches, with the aim to separate these patches by identifying the distribution of each patch. We localize forged regions by visualizing the movement of removed objects using anomalous patches. We conduct our experiments at both pixel and video levels to determine the effectiveness and efficiency of our approach to detection of video forgery. The experimental results show that our approach achieves excellent detection performance with low-computational complexity and leads to robust results for compressed and low-resolution videos.

In digital image steganography, the statistical model of an image is essential for hiding data in less detectable regions and achieving better security. This has been addressed in the literature where different cost-based and statistical model-based approaches were proposed. However, due to the usage of heuristically defined distortions and non-constrained message models, resulting in numerically solvable equations, there is no closed-form expression for security as a function of payload. The closed-form expression is crucial for a better insight into image steganography problem and also improving performance of batch steganography algorithms. Here, we develop a statistical framework for image steganography in which the cover and the stego messages are modeled as multivariate Gaussian random variables. We propose a novel Gaussian embedding model by maximizing the detection error of the most common optimal detectors within the adopted statistical model. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that improves empirical results of current cost-based and statistical model-based approaches. This methodology and its presented solution, by reason of assuming a continuous hidden message, remains the same for any embedding scenario. Afterward, the closed-form detection error is derived within the adopted model for image steganography and it is extended to batch steganography. Thus, we introduce Adaptive Batch size Image Merging steganographer, AdaBIM, and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

Image search engines commonly employ the Bag Of Features (BOF) method to represent each database image with a feature vector and retrieve the best candidate using a measure of similarity to a query image vector. The BOF vector, which specifies the occurrence frequency of features, is used with Soft Assignment (SA) to find the most similar candidates which are further analyzed using geometric information to determine the final location. In this paper, we propose a new method where partial geometric information captured in the scales of keypoints associated to feature descriptors is directly used in the feature vector entries, unlike the conventional BOF method which uses the frequency of features. The proposed method, referred to as Bag Of Scale-Indexed Features (BOSIF), is implemented with an algorithm devised to avoid the increased use of memory. A procedure for evaluating scale consistency between query and dataset images is also proposed. Experimental results demonstrate that BOSIF outperforms SA-based feature-indexed BOF method and has performance comparable to state-of-the-art approaches in terms of Recall and especially for the first retrieved images by removing false matches caused by quantization error.

This paper presents a novel approach to increase the performance bounds of image steganography under the criteria of minimizing distortion. The proposed approach utilizes a steganalysis convolutional neural network (CNN) framework to understand an image's model and embed in less detectable regions to preserve the model. In other word, the trained steganalysis CNN is used to calculate derivatives of the statistical model of an image with respect to embedding changes. The experimental results show that the proposed algorithm outperforms previous state-of-the-art methods in a wide range of low relative payloads when compared with HUGO, S-UNIWARD, and HILL by the state-of-the-art steganalysis.

A very important class of spatial queries consists of nearest-neighbor (NN) query and its variations. Many studies in the past decade utilize R-trees as their underlying index structures to address NN queries efficiently. The general approach is to use R-tree in two phases. First, R-tree's hierarchical structure is used to quickly arrive to the neighborhood of the result set. Second, the R-tree nodes intersecting with the local neighborhood ( Search Region ) of an initial answer are investigated to find all the members of the result set. While R-trees are very efficient for the first phase, they usually result in the unnecessary investigation of many nodes that none or only a small subset of their including points belongs to the actual result set. On the other hand, several recent studies showed that the Voronoi diagrams are extremely efficient in exploring an NN search region, while due to lack of an efficient access method, their arrival to this region is slow. In this paper, we propose a new index structure, termed VoR-Tree that incorporates Voronoi diagrams into R-tree, benefiting from the best of both worlds. The coarse granule rectangle nodes of R-tree enable us to get to the search region in logarithmic time while the fine granule polygons of Voronoi diagram allow us to efficiently tile or cover the region and find the result. Utilizing VoR-Tree, we propose efficient algorithms for various Nearest Neighbor queries, and show that our algorithms have better I/O complexity than their best competitors.

In this article, we first introduce the concept of Spatial Skyline Queries (SSQ). Given a set of data points P and a set of query points Q , each data point has a number of derived spatial attributes each of which is the point's distance to a query point. An SSQ retrieves those points of P which are not dominated by any other point in P considering their derived spatial attributes. The main difference with the regular skyline query is that this spatial domination depends on the location of the query points Q . SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in P and Q . Consequently, we reduce the complexity of SSQ search from O (| P | 2 | Q |) to O (| S | 2 | C | + √| P |), where | S | and | C | are the solution size and the number of vertices of the convex hull of Q , respectively. Considering Euclidean distance, we propose two algorithms, B 2 S 2 and VS 2 , for static query points and one algorithm, VCS 2 , for streaming Q whose points change location over time (e.g., are mobile). VCS 2 exploits the pattern of change in Q to avoid unnecessary recomputation of the skyline and hence efficiently perform updates. We also propose two algorithms, SNS 2 and VSNS 2 , that compute the spatial skyline with respect to the network distance in a spatial network database. Our extensive experiments using real-world datasets verify that both R-tree-based B 2 S 2 and Voronoi-based VS 2 outperform the best competitor approach in terms of both processing time and I/O cost. Furthermore, their output computed based on Euclidean distance is a good approximation of the spatial skyline in network space. For accurate computation of spatial skylines in network space, our experiments showed the superiority of VSNS 2 over SNS 2 .

A frequent query in geospatial planning and decision making domains (e.g., emergency response, data acquisition, street cleaning), is to find an optimal traversal plan (OTP) that traverses an entire area (e.g., a city) by navigating through all its streets. The optimality is defined in terms of the time it takes to complete the traversal. This time depends on the number of times each street segment is traversed as well as the navigation time such as the time spent on changing direction at each intersection.