Realistic animation of various interactions between multiple fluids, possibly undergoing phase change, is a challenging task in computer graphics. The visual scope of multi-phase multi-fluid phenomena covers complex tangled surface structures and rich color variations, which can greatly enhance visual effect in graphics applications. Describing such phenomena requires more complex models to handle challenges involving calculation of interactions, dynamics and spatial distribution of multiple phases, which are often involved and hard to obtain real-time performance. Recently, a diverse set of algorithms have been introduced to implement the complex multi-fluid phenomena based on the governing physical laws and novel discretization methods to accelerate the overall computation while ensuring numerical stability. By sorting through the target phenomena of recent research in the broad subject of multiple fluid, this state-of-the-art report summarizes recent advances on multi-fluid simulation in computer graphics.

Hybrid approaches such as combining video data with pure physics-based simulation have been popular in the recent decade for computer graphics. The key motivation is to clearly retain salient advantages from both data-driven method and model-centric numerical simulation, while overcoming certain difficulties of both. The Eulerian method, which has been widely employed in flow simulation, stores variables such as velocity and density on regular Cartesian grids, so it could be associated with (volumetric) video data on the same domain. This paper proposes a novel method for flow simulation, that is tightly coupling video-based reconstruction with physically-based simulation and making use of meaningful physical attributes during re-simulation. First, we reconstruct the density field from a single-view video. Second, we estimate the velocity field using the reconstructed density field as prior. In the iterative process, the pressure projection can be treated as a physical constraint and the results of each step are corrected by obtained velocity field in the Eulerian framework. Third, we use the reconstructed density field and velocity field to guide the Eulerian simulation with anticipated new results. Through the guidance of video data, we can produce new flows that closely match with the real scene exhibited in data acquisition. Moreover, in the multigrid Eulerian simulation, we can generate new visual effects which can't be created from raw video acquisition, with a goal of easily producing many more visually interesting results and respecting true physical attributes at the same time. We demonstrate salient advantages of our hybrid method with a variety of animation examples.

We propose an automatic garment seam modeling framework to create a garment model with the seam structure from a single image. In order to achieve this, a marked seam image database and parametric seam models have been set up. Given a real seam image, we first identify the type of the seam image based on our marked seam image database and seam parameters are parsed automatically by our sewing thread estimation method. Then the seam initial model is generated through the pre-defined seam parametric models. A garment model with the seam structure is finally obtained based on the seam position information which users have marked on the garment. Moreover, we verify the effectiveness of our method with numerous experiments.

This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Per-frame amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.

In motion estimation, illumination change is always a troublesome obstacle, which often causes severely performance reduction of optical flow computation. The essential reason is that most of estimation methods fail to formalize a unified definition in color or gradient domain for diverse environmental changes. In this paper, we propose a new solution based on deep convolutional networks to solve the key issue. Our idea is to train deep convolutional networks to represent the complex motion features under illumination change, and further predict the final optical flow fields. To this end, we construct a training dataset of multi-exposure image pairs by performing a series of non-linear adjustments in the traditional datasets of optical flow estimation. Our end-to-end network model consists of three main components:low-level feature network, fusion feature network, and motion estimation network. The former two components belong to the contracting part of our model in order to extract and represent the multi-exposure motion features; the third component is the expanding part of our model in order to learn and predict the high-quality optical flow. Compared with many state-of-the-art methods, our motion estimation based on deep convolutional networks can eliminate the obstacle of illumination change and yield optical flow results with competitive accuracy and time efficiency. Moreover, the good performance of our model is also demonstrated in some multi-exposure video applications, like HDR (High Dynamic Range) composition and flicker removal.

Image smoothing is a crucial image processing topic in image processing and has wide applied backgrounds. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance in many situations because textures containing obvious edges and large gradient changes are easy to be preserved as the main edges. In this paper, we propose a novel framework for image smoothing combined with the constraint of sparse high frequency gradient for texture image. First, we decompose the image into two components:a smooth component (constant component) and a non-smooth (high frequency) component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction and image composition.

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face should be similar with that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our method can learn and generate high-quality identity-preserving facial images with specified attributes.

In this paper, we present a practical system to automatically suggest the most pairing clothing items, given the reference clothing (upper-body or low-body). However this has being a challenge, due to having varieties of clothing categories. Clothing is one of the most informative cues for human appearance. In our daily life, people need to wear properly and beautifully to show their confidence, politeness and social status in various occasion. But, it is not easy to decide to decide on what and how to wear at the same time to match with the selected clothes. To address this problem, we propose a quadruple network architecture, where one dual network adopts Siamese convolution neural network architecture. Training examples are pairs of upper-body and low-body clothing items that are either compatible or incompatible. Another dual convolution neural network is used to learn clothing style features of the input image. This framework allows learning a feature transformation from the images of clothing items to two latent spaces, which we called compatible space and style space. After training the two dual networks, we use a distance fusion method to fuse the features extracted from the compatible and style dual networks. To acquire an optimized model and verify our proposed method, we expand a large clothing dataset called "How to Wear Beautifully" (H2WB). Experiments on H2WB dataset demonstrated that our learning model are effective with feature distance fusion and clothing item pairing recommendation.

Bitcoin has gained its popularity for almost ten years as a "secure and anonymous digital currency", but according to several recent researches we know that it can only provide pseudonymity rather than real anonymity, and privacy has been one of the main concerns in the system similar to Bitcoin. Ring signature is a good method for those users who need better anonymity in cryptocurrency. It was first proposed by Rivest et al. based upon the discrete logarithm problem (DLP) assumption in 2006, which allows a user to sign a message anonymously on behalf of a group of users even without their coordination. The size of ring signature is one of the dominating parameters, and constant-size ring signature (where signature size is independent of the ring size) is much desirable. Otherwise, when the ring size is large, the resultant ring signature becomes unbearable for power limited devices or lead to heavy burden over the communication network. Though being extensively studied, currently there are only two approaches for constant-size ring signature. Achieving practical constant-size ring signature is a long-standing open problem since its introduction. In this work, we solve this open question. We present a new constant-size ring signature scheme based on bilinear pairing and accumulators, which is provably secure under the random oracle (RO) model. To the best of our knowledge, it stands for the most practical ring signature up to now.

Blockchain is becoming popular as a distributed and reliable ledger which allows distrustful parties to transact safely without trusting third parties. Emerging blockchain systems like Ethereum support smart contracts where miners can run arbitrary user-defined programs. However, one of the biggest concerns about the blockchain and the smart contract is privacy, since all the transactions on the chain are exposed to the public. In this paper, we present ShadowEth, a system that leverages hardware enclave to ensure the confidentiality of smart contracts while keeping the integrity and availability based on existing public blockchains like Ethereum. ShadowEth establishes a confidential and secure platform protected by Trusted Execution Environment (TEE) off the public blockchain for the execution and storage of private contracts. It only puts the process of verification on the blockchain. We provide a design of our system including a protocol of the cryptographic communication and verification and show the applicability and feasibility of the ShadowEth by various case studies. We implement a prototype using the Intel SGX on the Ethereum network and analyze the security and availability of the system.

With the development of network technology and cloud computing, data sharing is becoming increasingly popular, and many scholars have conducted in-depth research to promote its flourish. As the scale of data sharing expands, its privacy protection has become a hot issue in research. Moreover, in data sharing, the data is usually maintained in multiple parties, which brings new challenges to protect the privacy of these multi-party data. In this paper, we propose a trusted data sharing scheme using blockchain. We use blockchain to prevent the shared data from being tampered with, and use the Paillier cryptosystem to realize the confidentiality of the shared data. In the proposed scheme, the shared data can be traded, and the transaction information is protected by using the (p, t)-threshold Paillier cryptosystem. We conduct experiments in cloud storage scenarios and the experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

An effective and secure system used for evidence preservation is essential to possess the properties of anti-loss, anti-forgery, anti-tamper as well as perfect verifiability. Traditional architecture which relies on centralized cloud storage is depressingly beset by the security problems such as incomplete confidence and unreliable regulation. Moreover, an expensive, inefficient and incompatible design impedes the effort of evidence preservation. In contrast, the decentralized blockchain network is qualified as a perfect replacement for its secure anonymity, irrevocable commitment and transparent traceability. Combined with subliminal channels in blockchain, we have weaved the transaction network with newly designed evidence audit network. In this paper, we have presented and implemented a lightweight digital evidence-preservation architecture which possesses the features of privacy-anonymity, audit-transparency, function-scalability and operation-lightweight. The anonymity is naturally formed from the cryptographic design, since the cipher evidence under encrypted cryptosystem and hash based functions leakages nothing to the public. Covert channels are efficiently excavated to optimize the cost, connectivity and security of the framework, transforming the great computation power of Bitcoin network to the value of credit. The transparency used for audit, which relates to the proof of existence, comes from instant timestamps and irreversible hash functions in mature blockchain network. The scalability is represented by the evidence chain interacted with the original blockchain, and the extended chains on top of mainchain will cover the most of auditors in different institutions. And the lightweight, which is equal to low-cost, is derived from our fine-grained hierarchical services. At last, analyses of efficiency, security, and availability have shown the complete accomplishment of our system.

The double fetch problem occurs when the data is maliciously changed between two kernel reads of supposedly the same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices (aka. The Hardware Double Fetch). Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O (Input and Output) memory, and due to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We proposed a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results were categorized and each category was analyzed using case studies to discuss the possibility of causing bugs. We also found 4 double-fetch vulnerabilities, which have been confirmed and fixed by the maintainers as a result of our report.

As trust becomes increasingly important in software domain, software trustworthiness-as a complex highcomposite concept, has developed into a big challenge people have to face, especially in the current open, dynamic and ever-changing Internet environment. Furthermore, how to recognize and define trust problem from its nature and how to measure software trustworthiness correctly and effectively play a key role in improving users' trust in choosing software. Based on trust theory in the field of humanities and sociology, this paper proposes a measurable S2S (Social-to-Software) software trustworthiness framework, introduces a generalized indicator loss to unify three parts of trustworthiness result, and presents a whole metric solution for software trustworthiness, including the advanced J-M model based on power function and time-loss rate for ability trustworthiness measurement, the fuzzy comprehensive evaluation advanced-model considering effect of multiple short boards for basic standard trustworthiness, and the identity trustworthiness measurement method based on the code homology detecting tools. Finally, it provides a case study to verify that the solution is applicable and effective.