Currently, multimodal biometric recognition technology is receiving increasing attention. Most existing multimodal biometric recognition techniques require complete multimodal data during both the training and testing phases. However, due to various limitations, it is challenging to obtain complete and high-quality multimodal biometric data. To address this problem, we proposed a Mutual Distillation Framework (MDF) for palmprint and palmvein based multimodal biometric recognition with uncertain missing modality. Specifically, we firstly design an intra-inter modal data missing augmentation mechanism to generate heterogeneous missing samples. Moreover, a mutual knowledge distillation module is introduced to establish correlation of beneficial semantic information across different missing scenarios. In particular, we incorporate a Sample Semantic Discrepancy Guided mechanism within the mutual distillation framework, which calculates the semantic discrepancy between samples under corresponding heterogeneous missing patterns to encourage the model to focus on samples containing more complementary information. Experimental results demonstrate that the proposed model outperforms state-of-the-art incomplete multimodal learning models across three multimodal biometric benchmark datasets.

Virtual human avatars are essential for applications such as gaming, augmented reality, and virtual production. However, existing methods struggle to achieve high fidelity reconstruction from monocular input while keeping hardware costs low. Many approaches rely on the SMPL body prior and apply vertex offsets to represent clothed avatars. Unfortunately, excessive offsets often cause misalignment and blurred contours, particularly around clothing wrinkles, silhouette boundaries, and facial regions. To address these limitations, we propose a dual branch framework for human avatar reconstruction from monocular video. A lightweight Vertex Align Net (VAN) predicts per-vertex normal direction offsets on the SMPL mesh to achieve coarse geometric alignment and guide Gaussian-based human avatar modeling. In parallel, we construct a high resolution facial Gaussian branch based on FLAME estimated parameters, with facial regions localized via pretrained detectors. The facial and body renderings are fused using a semantic mask to enhance facial clarity and ensure globally consistent avatar appearance. Experiments demonstrate that our method surpasses state of the art approaches in modeling animatable human avatars with fine grained fidelity.

The iterative Wiener filter (IWF) algorithm can achieve a fast convergence rate. However, its performance may degrade when it encounters noisy input scenarios. To tackle this problem, a novel IWF algorithm incorporating bias-compensation (BC-IWF) is proposed, which can enhance the performance of the algorithm by estimating the input noise variance. The BC-IWF algorithm optimizes the step size for each iteration and updates along the direction of the gradient. To further reduce the steady-state error, a normalized IWF by making use of the bias-compensation scheme (BC-NIWF) algorithm is proposed. Moreover, the steady-state performance of the BC-NIWF algorithm is analyzed. Simulation results demonstrate the validity of the theoretical analysis and the BC-NIWF algorithm achieves improved misadjustment compared with the state-of-the-art algorithms.

Multimodal biometric recognition has shown great potential in identity authentication tasks and has attracted increasing interest recently. Currently, most existing multimodal biometric recognition algorithms require test samples with complete multimodal data. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practical scenarios. To this end, we proposed a hierarchical cross-modal image generation for palmprint and palmvein based multimodal biometric recognition with missing modality. First, a hierarchical cross-modal image generation model is designed to achieve the pixel alignment of different modalities and reconstruct the image information of missing modality. Specifically, a cross-modal texture transfer network is utilized to implement the texture style transformation between different modalities, and then a cross-modal structure generation network is proposed to establish the correlation mapping of structural information between different modalities. Second, multimodal dynamic sparse feature fusion model is presented to obtain more discriminative and reliable representations, which can also enhance the robustness of our proposed model to dynamic changes in image quality of different modalities. The proposed model is evaluated on three multimodal biometric benchmark datasets, and experimental results demonstrate that our proposed model outperforms recent mainstream incomplete multimodal learning models.

ABSTRACT Laser fault injection has been proved to be a useful tool for attacks on integrated circuits. Transistors hit by a pulse of photons causes them to conduct transiently, thereby introducing transient logic errors, such as register value modifications, memory dumping, and so on. Attackers can make use of this abnormal behavior and extract sensitive information that the devices try to protect. This paper demonstrates laser fault injection attacks on very‐large‐scale integration circuits in a semi‐invasive way for the purpose of validating fault tolerant design and performance. Then, the paper presents a simulation methodology to evaluate the dependability of the integrated circuit design against laser fault injection attacks at design time. This simulation methodology involves exhaustively scanning the layout, incorporating the exposed cells into a circuit simulator, and examining the response of the circuit in detail. Experiments conducted on the same test chip spot the same vulnerabilities, thus indicating the validity of the proposed simulation methodology. Copyright © 2011 John Wiley & Sons, Ltd.

Numerous research efforts have been made recently on exploiting the randomness of wireless channels to generate secret keys. This channel state information (CSI) based key generation method relies on spatial independence between the legitimate and eavesdropping channels. In this paper, we propose a methodology to extract secret keys through side-channel information. Our methodology involves the capture of side-channel information leaked by the electronic instruments, either when measuring channel characteristics or during encryption/decryption for key confirmation. Secret keys are extracted via analysis of the side-channel information. We provide experiment results to demonstrate the feasibility of our proposed side-channel attack methodology.

An automatic test-generation tool can produce a large number of test inputs to exercise the class under test. However, without specifications, developers cannot inspect the execution of each automatically generated test input practically. To address the problem, we have developed an automatic test abstraction tool, called Abstra, to extract high level object-state-transition information from unit-test executions, without requiring a priori specifications. Given a class and a set of its generated test inputs, our tool extracts object state machines (OSM): a state in an OSM represents an object state of the class and a transition in an OSM represents method calls of the class. When an object state in an OSM is concrete (being represented by the values of all fields reachable from the object), the size of the OSM could be too large to be useful for inspection. To address this issue, we have developed techniques in the tool to abstract object states based on returns of observer methods, branch coverage of methods, and individual object fields, respectively. The tool provides useful object-state-transition information for programmers to inspect unit-test executions effectively. In particular, the tool helps facilitate correctness inspection, program understanding, fault isolation, and test characterization.