arXiv subjects

Weijie Wang

Publications and source records attributed to Weijie Wang.

83 records · Page 5Linked to original sources

arXiv2023

LibAM: An Area Matching Framework for Detecting Third-party Libraries in Binaries

Third-party libraries (TPLs) are extensively utilized by developers to expedite the software development process and incorporate external functionalities. Nevertheless, insecure TPL reuse can lead to significant security risks. Existing methods are employed to determine the presence of TPL code in the target binary. Existing methods, which involve extracting strings or conducting function matching, are employed to determine the presence of TPL code in the target binary. However, these methods often yield unsatisfactory results due to the recurrence of strings and the presence of numerous similar non-homologous functions. Additionally, they struggle to identify specific pieces of reused code in the target binary, complicating the detection of complex reuse relationships and impeding downstream tasks. In this paper, we observe that TPL reuse typically involves not just isolated functions but also areas encompassing several adjacent functions on the Function Call Graph (FCG). We introduce LibAM, a novel Area Matching framework that connects isolated functions into function areas on FCG and detects TPLs by comparing the similarity of these function areas. Furthermore, LibAM is the first approach capable of detecting the exact reuse areas on FCG and offering substantial benefits for downstream tasks. Experimental results demonstrate that LibAM outperforms all existing TPL detection methods and provides interpretable evidence for TPL detection results by identifying exact reuse areas. We also evaluate LibAM's accuracy on large-scale, real-world binaries in IoT firmware and generate a list of potential vulnerabilities for these devices. Last but not least, by analyzing the detection results of IoT firmware, we make several interesting findings, such as different target binaries always tend to reuse the same code area of TPL.

cs.SE↗

arXiv2023

Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection

Malicious use of deepfakes leads to serious public concerns and reduces people's trust in digital media. Although effective deepfake detectors have been proposed, they are substantially vulnerable to adversarial attacks. To evaluate the detector's robustness, recent studies have explored various attacks. However, all existing attacks are limited to 2D image perturbations, which are hard to translate into real-world facial changes. In this paper, we propose adversarial head turn (AdvHeat), the first attempt at 3D adversarial face views against deepfake detectors, based on face view synthesis from a single-view fake image. Extensive experiments validate the vulnerability of various detectors to AdvHeat in realistic, black-box scenarios. For example, AdvHeat based on a simple random search yields a high attack success rate of 96.8% with 360 searching steps. When additional query access is allowed, we can further reduce the step budget to 50. Additional analyses demonstrate that AdvHeat is better than conventional attacks on both the cross-detector transferability and robustness to defenses. The adversarial images generated by AdvHeat are also shown to have natural looks. Our code, including that for generating a multi-view dataset consisting of 360 synthetic views for each of 1000 IDs from FaceForensics++, is available at https://github.com/twowwj/AdvHeaT.

cs.CV↗

arXiv2023

T2TD: Text-3D Generation Model based on Prior Knowledge Guidance

In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem. In this paper, inspired by the ability of human beings to complement visual information details from ambiguous descriptions based on their own experience, we propose a novel text-3D generation model (T2TD), which introduces the related shapes or textual information as the prior knowledge to improve the performance of the 3D generation model. In this process, we first introduce the text-3D knowledge graph to save the relationship between 3D models and textual semantic information, which can provide the related shapes to guide the target 3D model generation. Second, we integrate an effective causal inference model to select useful feature information from these related shapes, which removes the unrelated shape information and only maintains feature information that is strongly relevant to the textual description. Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model. The final experimental results demonstrate that our approach significantly improves 3D model generation quality and outperforms the SOTA methods on the text2shape datasets.

cs.CV↗

arXiv2023

RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration

The Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability. Owing to these properties, the Swin Transformer is suitable for establishing more distant relationships between corresponding voxels in different positions in complex abdominal image registration tasks. However, the registration models based on transformers combine multiple voxels into a single semantic token. This merging process limits the transformers to model and generate coarse-grained spatial information. To address this issue, we propose Recovery Feature Resolution Network (RFRNet), which allows the transformer to contribute fine-grained spatial information and rich semantic correspondences to higher resolution levels. Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows. Therefore, we present a Weighted Window Attention (WWA) to build global interactions between windows automatically. It is implemented after the regular and cyclic shift window partitioning operations within the Swin transformer block. The proposed unsupervised deformable image registration model, named RFR-WWANet, detects the long-range correlations, and facilitates meaningful semantic relevance of anatomical structures. Qualitative and quantitative results show that RFR-WWANet achieves significant improvements over the current state-of-the-art methods. Ablation experiments demonstrate the effectiveness of the RFRNet and WWA designs. Our code is available at \url{https://github.com/MingR-Ma/RFR-WWANet}.

cs.CV↗

arXiv2023

Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration

Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Specifically, we first adopt a network to learn posterior probability distributions of Gaussian mixture models (GMMs) from point clouds. To handle partial point cloud registration, we apply the Sinkhorn algorithm to predict the distribution-level correspondences under the constraint of the mixing weights of GMMs. To enable unsupervised learning, we design three distribution consistency-based losses: self-consistency, cross-consistency, and local contrastive. The self-consistency loss is formulated by encouraging GMMs in Euclidean and feature spaces to share identical posterior distributions. The cross-consistency loss derives from the fact that the points of two partially overlapping point clouds belonging to the same clusters share the cluster centroids. The cross-consistency loss allows the network to flexibly learn a transformation-invariant posterior distribution of two aligned point clouds. The local contrastive loss facilitates the network to extract discriminative local features. Our UDPReg achieves competitive performance on the 3DMatch/3DLoMatch and ModelNet/ModelLoNet benchmarks.

cs.CV↗

arXiv2022

Economic Potential for Hybrid Electric Vehicles in Urban Signal-free Intersections with Decentralized MPC

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper provides a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control, which is able to ensure a rapid search and unique solution in real time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results show that the traveling cost of sHEVs approaches and even under some circumstances reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.

eess.SY↗

arXiv2022

Highly dynamic locomotion control of biped robot enhanced by swing arms

Swing arms have an irreplaceable role in promoting highly dynamic locomotion on bipedal robots by a larger angular momentum control space from the viewpoint of biomechanics. Few bipedal robots utilize swing arms and its redundancy characteristic of multiple degrees of freedom due to the lack of appropriate locomotion control strategies to perfectly integrate modeling and control. This paper presents a kind of control strategy by modeling the bipedal robot as a flywheel-spring loaded inverted pendulum (F-SLIP) to extract characteristics of swing arms and using the whole-body controller (WBC) to achieve these characteristics, and also proposes a evaluation system including three aspects of agility defined by us, stability and energy consumption for the highly dynamic locomotion of bipedal robots. We design several sets of simulation experiments and analyze the effects of swing arms according to the evaluation system during the jumping motion of Purple (Purple energy rises in the east)V1.0, a kind of bipedal robot designed to test high explosive locomotion. Results show that Purple's agility is increased by more than 10 percent, stabilization time is reduced by a factor of two, and energy consumption is reduced by more than 20 percent after introducing swing arms.

cs.RO↗

arXiv2021

Network Effects Research: A Systematic Review of Theoretical Mechanisms and Measures

This article contributes to the network effectiveness literature by identifying the theoretical mechanisms and network measures scholars in public administration and policy use to draw inferences between network structures and network effects. We conducted a systematic review of empirical network effects research in 40 journals of public administration and policy from 1998 to 2019. We reviewed and coded 89 articles and described the main social theories used in the network effectiveness literature and the associated mechanisms that translate network structures to network effects. We also explain how scholars operationalize those theoretical mechanisms through network measures. Overall, our findings reflect that there is a limited use of social theories for the explanation of network effects and in some cases, there is an inconsistent use of network measures associated with theories. Moreover, we identify the main challenges related to network effects, including the difficulty of isolating specific mechanisms related to a particular social theory, the use of network structures both as a mechanism and as a measure, and the lack of data to examine network dynamics and coevolution.

cs.SI↗

arXiv2021

A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here we propose a novel framework, named AIMEE, integrating AI Model and Enzymology Experiments, to identify inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value less than 3 μM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and expanding the boundaries of drug discovery.

q-bio.MN↗

arXiv2018

An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting

Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for a feedforward inference pass. These values are, however, proxy measures since empirical performance in actual deployments is determined by many factors. In this paper, we study the power consumption of a family of convolutional neural networks for keyword spotting on a Raspberry Pi. We find that both proxies are good predictors of energy usage, although the number of multiplies is more predictive than the number of model parameters. We also confirm that models with the highest accuracies are, unsurprisingly, the most power hungry.

cs.OH↗

arXiv2016

Alternating Direction Method of Multipliers for Linear Inverse Problems

In this paper we propose an iterative method using alternating direction method of multipliers (ADMM) strategy to solve linear inverse problems in Hilbert spaces with general convex penalty term. When the data is given exactly, we give a convergence analysis of our ADMM algorithm without assuming the existence of Lagrange multiplier. In case the data contains noise, we show that our method is a regularization method as long as it is terminated by a suitable stopping rule. Various numerical simulations are performed to test the efficiency of the method.

math.NA↗