At work, at home, and in some public places, a desktop PC is usually available nowadays. Therefore, it is important for users to be able to play various videos on different PCs smoothly, but the diversity of codec types complicates the situation. Although some mainstream media players can try to download the needed codec automatically, this may fail for average users because installing the codec usually requires administrator privileges to complete, while the user may not be the owner of the PC. We believe an ideal solution should work without users' intervention, and need no special privileges. This paper proposes such a user-friendly, program-transparent solution for Windows-based media players. It runs the media player in a user-mode virtualization environment, and then downloads the needed codec on-the-fly. Because of API (Application Programming Interface) interception, some resource-accessing API calls from the player will be redirected to the downloaded codec resources. Then from the viewpoint of the player, the necessary codec exists locally and it can handle the video smoothly, although neither system registry nor system folders was modified during this process. Besides convenience, the principle of least privilege is maintained and the host system is left clean. This paper completely analyzes the technical issues and presents such a prototype which can work with DirectShow-compatible players. Performance tests show that the overhead is negligible. Moreover, our solution conforms to the Software-As-A-Service (SaaS) mode, which is very promising in the Internet era.

The development of network technology reveals the clear trend that mobile devices will soon be equipped with more and more network-based functions and services. This increase also results in more intrusions and attacks on mobile devices; therefore, mobile security mechanisms are becoming indispensable. In this paper, we propose a novel signature matching scheme for mobile security. This scheme not only emphasizes a small resource requirement and an optimal scan speed, which are both important for resource-limited mobile devices, but also focuses on practical features such as stable performance, fast signature set updates and hardware implementation. This scheme is based on the finite state machine (FSM) approach widely used for string matching. An SRAM-based two-level finite state machine (TFSM) solution is introduced to utilize the unbalanced transition distribution in the original FSM to decrease the memory requirement, and to shorten the critical path of the single-FSM solution. By adjusting the boundary of the two FSMs, optimum memory usage and throughput are obtainable. The hardware circuit of our scheme is designed and evaluated by both FPGA and ASIC technology. The result of FPGA evaluation shows that 2,168 unique patterns with a total of 32,776 characters are stored in 177.75 KB SelectRAM blocks of Xilinx XC4VLX40 FPGA and a 3.0 Gbps throughput is achieved. The result of ASIC evaluation with 180 nm-CMOS library shows a throughput of over 4.5 Gbps with 132 KB of SRAM. Because of the small amount of memory and logic cell requirements, as well as the scalability of our scheme, higher performance is achieved by instantiating several signature matching engines when more resources are provided.

We present a new framework for embedding holographic halftone watermarking data into images by fusion of scale-related wavelet coefficients. The halftone watermarking image is obtained by using error-diffusion method and converted into Fresnel hologram, which is considered to be the initial password. After encryption, a scrambled watermarking image through Arnold transform is embedded into the host image during the halftoning process. We characterize the multi-scale representation of the original image using the discrete wavelet transform. The boundary information of the target image is fused by correlation of wavelet coefficients across wavelet transform layers to increase the pixel resolution scale. We apply the inter-scale fusion method to gain fusion coefficient of the fine-scale, which takes into account both the detail of the image and approximate information. Using the proposed method, the watermarking information can be embedded into the host image with recovery against the halftoning operation. The experimental results show that the proposed approach provides security and robustness against JPEG compression and different attacks compared to previous alternatives.

This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

Universal filtered multicarrier (UFMC) systems offer a flexibility of filtering sub-bands with arbitrary bandwidth to suppress out-of-band (OoB) emission, while keeping the orthogonality between subcarriers in one sub-band. Oscillator discrepancies between the transmitter and receiver induce carrier frequency offset (CFO) in practical systems. In this paper, we propose a novel CFO estimation method for UFMC systems that has very low computational complexity and can then be used in practical systems. In order to fully exploit the coherence bandwidth of the channel, the training symbols are designed to have several identical segments in the frequency domain. As a result, the integral part of CFO can be estimated by simply determining the correlation between received signal and the training symbol. Simulation results show that the proposed method can achieve almost the same performance as an existing method and even a better performance in channels that have small decay parameter values. The proposed method can also be used in other multicarrier systems, such as orthogonal frequency division multiplexing (OFDM).

Segmenting foreground objects in unconstrained dynamic scenes still remains a difficult problem. We present a novel unsupervised segmentation approach that allows robust object segmentation of dynamic scenes with large displacements. To make this possible, we project motion based foreground region hypotheses generated via standard optical flow onto visual saliency regions. The motion hypotheses correspond to inside seeds mapping of the motion boundary. For visual saliency, we generalize the image signature method from images to videos to delineate saliency mapping of object proposals. The mapping of image signatures estimated in Discrete Cosine Transform (DCT) domain favor stand-out regions in the human visual system. We leverage a Markov random field built on superpixels to impose both spatial and temporal consistence constraints on the motion-saliency combined segments. Projecting salient regions via an image signature with inside mapping seeds facilitates segmenting ambiguous objects from unconstrained dynamic scenes in presence of large displacements. We demonstrate the performance on fourteen challenging unconstrained dynamic scenes, compare our method with two state-of-the-art unsupervised video segmentation algorithms, and provide quantitative and qualitative performance comparisons.

Cold boot attack is a side channel attack that recovers data from memory, which persists for a short period after power is lost. In the course of this attack, the memory gradually degrades over time and only a corrupted version of the data may be available to the attacker. Recently, great efforts have been made to reconstruct the original data from a corrupted version of AES key schedules, based on the assumption that all bits in the charged states tend to decay to the ground states while no bit in the ground state ever inverts. However, in practice, there is a small number of bits flipping in the opposite direction, called reverse flipping errors. In this paper, motivated by the latest work that formulates the relations of AES key bits as a Boolean Satisfiability problem, we move one step further by taking the reverse flipping errors into consideration and employing off-the-shelf SAT and MaxSAT solvers to accomplish the recovery of AES-128 key schedules from decayed memory images. Experimental results show that, in the presence of reverse flipping errors, the MaxSAT approach enables reliable recovery of key schedules with significantly less time, compared with the SAT approach that relies on brute force search to find out the target errors. Moreover, in order to further enhance the efficiency of key recovery, we simplify the original problem by removing variables and formulas that have relatively weak relations to the whole key schedule. Experimental results demonstrate that the improved MaxSAT approach reduces the scale of the problem and recover AES key schedules more efficiently when the decay factor is relatively large.