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The visual security index (VSI) is a quantized indicator for objective visual security evaluation of selectively encrypted images. One challenging problem in current research is that the performance of VSIs is highly sensitive to the extracted features and the method of similarity measurement, and it is hard to choose appropriate handcrafted features from encrypted images, as well as to find an effective similarity measurement. In this paper, we make the first attempt to present a novel convolutional neural network-based visual security index (CNNVSI). Our proposed CNNVSI is purely data-driven and trained end-to-end. We propose three specialized designs to make the approach work for encrypted low-quality images without any handcrafted features or prior knowledge about the human vision system (HVS). First, we present a patch labeling algorithm to assign each encrypted patch a visual security score. Second, we design a multiscale attention residual network (MARNet) for feature learning. Last, we propose to fuse the learned features from plain images, encrypted images and their discrepancy images. Extensive and systematic experiments are conducted on five publicly available image databases to analyze the performance of our proposed CNNVSI, and the experimental results and their analysis demonstrate that our proposed CNNVSI significantly outperforms the existing state-of-the-art methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Zero-Shot Learning (ZSL) for visual recognition is typically achieved by exploiting a semantic embedding space. In such a space, both seen and unseen class labels as well as image features can be embedded so that the similarity among them can be measured directly. In this work, we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space. Existing ZSL works employ either euclidean or cosine distances. However, in a high-dimensional space where the projected class labels (prototypes) are sparse, these distances are suboptimal, resulting in a number of problems including hubness and domain shift. To overcome these problems, a novel manifold distance computed on a semantic class prototype graph is proposed which takes into account the rich intrinsic semantic structure, i.e., semantic manifold, of the class prototype distribution. To further alleviate the domain shift problem, a new regularisation term is introduced into a ranking loss based embedding model. Specifically, the ranking loss objective is regularised by unseen class prototypes to prevent the projected object features from being biased towards the seen prototypes. Extensive experiments on four benchmarks show that our method significantly outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Video analysis often begins with background subtraction. This problem is often approached in two steps—a background model followed by a regularisation scheme. A model of the background allows it to be distinguished on a per-pixel basis from the foreground, whilst the regularisation combines information from adjacent pixels. We present a new method based on Dirichlet process Gaussian mixture models, which are used to estimate per-pixel background distributions. It is followed by probabilistic regularisation. Using a non-parametric Bayesian method allows per-pixel mode counts to be automatically inferred, avoiding over-/under- fitting. We also develop novel model learning algorithms for continuous update of the model in a principled fashion as the scene changes. These key advantages enable us to outperform the state-of-the-art alternatives on four benchmarks. [ABSTRACT FROM PUBLISHER]

We study the parameter estimation of a nonlinear chaotic system, which can be essentially formulated as a multidimensional optimization problem. In this paper, an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy. Experiments are conducted on the Lorenz system and the Chen system. The proposed algorithm is used to estimate the parameters for these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained. [ABSTRACT FROM AUTHOR]

Abstract: This paper tackles the problem of surveillance video content modelling. Given a set of surveillance videos, the aims of our work are twofold: firstly a continuous video is segmented according to the activities captured in the video; secondly a model is constructed for the video content, based on which an unseen activity pattern can be recognised and any unusual activities can be detected. To segment a video based on activity, we propose a semantically meaningful video content representation method and two segmentation algorithms, one being offline offering high accuracy in segmentation, and the other being online enabling real-time performance. Our video content representation method is based on automatically detected visual events (i.e. ‘what is happening in the scene’). This is in contrast to most previous approaches which represent video content at the signal level using image features such as colour, motion and texture. Our segmentation algorithms are based on detecting breakpoints on a high-dimensional video content trajectory. This differs from most previous approaches which are based on shot change detection and shot grouping. Having segmented continuous surveillance videos based on activity, the activity patterns contained in the video segments are grouped into activity classes and a composite video content model is constructed which is capable of generalising from a small training set to accommodate variations in unseen activity patterns. A run-time accumulative unusual activity measure is introduced to detect unusual behaviour while usual activity patterns are recognised based on an online likelihood ratio test (LRT) method. This ensures robust and reliable activity recognition and unusual activity detection at the shortest possible time once sufficient visual evidence has become available. Comparative experiments have been carried out using over 10h of challenging outdoor surveillance video footages to evaluate the proposed segmentation algorithms and modelling approach. [Copyright &y& Elsevier]

Abstract: A block cryptographic scheme based on iterating a chaotic map is proposed. With random binary sequences generated from the real-valued chaotic map, the plaintext block is permuted by a key-dependent shift approach and then encrypted by the classical chaotic masking technique. Simulation results show that performance and security of the proposed cryptographic scheme are better than those of existing algorithms. Advantages and security of our scheme are also discussed in detail. [Copyright &y& Elsevier]

Given that most current structure from motion (SFM) algorithms cannot recover true motion estimates reliably, it is important to understand the impact such motion errors have on the shape reconstruction. In this paper, various robustness issues surrounding different types of second order shape estimates recovered from motion cue are addressed. We present a theoretical model to understand the impact that errors in the motion estimates have on shape recovery. Using this model, we focus on the recovery of second order shape under different generic motions, each of these motions presenting different degrees of error sensitivity. We also show that different shapes exhibit different degrees of robustness with respect to its recovery. Understanding such different distortion behavior is important if we want to design better fusion strategy with other shape cues. [Copyright &y& Elsevier]

An image compression and encryption algorithm by combining the advanced encryption standard (AES) with the hyper-chaotic system is designed, in which Arnold map is employed to eliminate part of the block effect in the image compression process. The original image is compressed with the assistance of a discrete cosine transform and then its transform coefficients are encrypted with the AES algorithm. Besides, the hyper-chaotic system is adopted to introduce the nonlinear process for image encryption. Numerical simulations and theoretical analyses demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion schemes have been studied in recent years. However, little work has been done in multi-focus image transmission security. This paper proposes a scheme that can reduce data transmission volume and resist various attacks. First, multi-focus image fusion based on wavelet decomposition can generate complete scene images and optimize the perception of the human eye. The fused images are sparsely represented with DCT and sampled with structurally random matrix (SRM), which reduces the data volume and realizes the initial encryption. Then the obtained measurements are further encrypted to resist noise and crop attack through combining permutation and diffusion stages. At the receiver, the cipher images can be jointly decrypted and reconstructed. Simulation results demonstrate the security and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

We propose a quality control method that replaces rate control in video coding and achieves quality control with the minimum bit rate. First, based on the rate–distortion model, we establish a Karush–Kuhn–Tucker equation considering the convergent properties of variational inequalities to solve the continuous convex problem. Second, combined with the rate–quality (R-Q) model, the proposed algorithm is implemented under versatile video coding (VVC) to obtain the optimal closed-form solution for the quantization parameters of a coding unit through the Lagrange multiplier method. Experiments reveal that our algorithm can achieve stable video coding quality while guaranteeing coding performance, achieving high quality control accuracy with constant quality. [ABSTRACT FROM AUTHOR]

Abstract: A novel image encryption algorithm based on logistic map is proposed recently. In this paper, a chosen plaintext attack on this algorithm is presented and some other flaws of the algorithm are pointed out. Theoretical analysis and experimental simulation indicate that the plain image can be recovered exactly from the cipher image without secret key. Therefore, this algorithm is not secure enough for practical applications. An improvement is proposed to enhance the security of the original algorithm. Simulation results and theoretical analysis show that the improved scheme has expected cryptographic properties and is more secure than the original algorithm. [Copyright &y& Elsevier]

One of the most interesting and desired capabilities for automated video behavior analysis is the identification of rarely occurring and subtle behaviors. This is of practical value because dangerous or illegal activities often have few or possibly only one prior example to learn from and are often subtle. Rare and subtle behavior learning is challenging for two reasons: 1) Contemporary modeling approaches require more data and supervision than may be available and 2) the most interesting and potentially critical rare behaviors are often visually subtle—occurring among more obvious typical behaviors or being defined by only small spatio-temporal deviations from typical behaviors. In this paper, we introduce a novel weakly supervised joint topic model which addresses these issues. Specifically, we introduce a multiclass topic model with partially shared latent structure and associated learning and inference algorithms. These contributions will permit modeling of behaviors from as few as one example, even without localization by the user and when occurring in clutter, and subsequent classification and localization of such behaviors online and in real time. We extensively validate our approach on two standard public-space data sets, where it clearly outperforms a batch of contemporary alternatives. [ABSTRACT FROM PUBLISHER]