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 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.

Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as required in many practical applications. In this paper, we propose a novel technique for video stabilization based on the particle filtering framework. Scale-invariant feature points are extracted to form a rough estimate which is used to model the importance density. We use a constant-velocity Kalman filter model to estimate intentional camera movement. We also prove that the particle filtering estimate will lower the error variance. The superior performance and robustness of our algorithm is demonstrated by computer simulations.

Synaptic dysfunction is hypothesized to be one of the earliest brain changes in Alzheimer’s disease, leading to “hyperexcitability” in neuronal circuits. In this study, we evaluated a novel hyperexcitation indicator (HI) for each brain region using a hybrid resting-state structural connectome to probe connectome-level excitation–inhibition balance in cognitively intact middle-aged apolipoprotein E (APOE) ε4 carriers with noncarriers (16 male/22 female in each group). Regression with three-way interactions (sex, age, and APOE-ε4 carrier status) to assess the effect of APOE-ε4 on excitation–inhibition balance within each sex and across an age range of 40–60 years yielded a significant shift toward higher HI in female carriers compared with noncarriers (beginning at 50 years). Hyperexcitation was insignificant in the male group. Further, in female carriers the degree of hyperexcitation exhibited significant positive correlation with working memory performance (evaluated via a virtual Morris Water task) in three regions: the left pars triangularis, left hippocampus, and left isthmus of cingulate gyrus. Increased excitation of memory-related circuits may be evidence of compensatory recruitment of neuronal resources for memory-focused activities. In sum, our results are consistent with known Alzheimer’s disease sex differences; in that female APOE-ε4 carriers have globally disrupted excitation–inhibition balance that may confer greater vulnerability to disease neuropathology.

In this paper, we develop a novel solution for particle filtering on general graphs. We provide an exact solution for particle filtering on directed cycle-free graphs. The proposed approach relies on a partial-order relation in an antichain decomposition that forms a high-order Markov chain over the partitioned graph. We subsequently derive a closed-form sequential updating scheme for conditional density propagation using particle filtering on directed cycle-free graphs. We also provide an approximate solution for particle filtering on general graphs by splitting graphs with cycles into multiple directed cycle-free subgraphs. We then use the sequential updating scheme by alternating among the directed cycle-free subgraphs to obtain an estimate of the density propagation. We rely on the proposed method for particle filtering on general graphs for two video tracking applications: 1) object tracking using high-order Markov chains; and 2) distributed multiple object tracking based on multi-object graphical interaction models. Experimental results demonstrate the improved performance of the proposed approach to particle filtering on graphs compared with existing methods for video tracking.