The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the pu...
With the widespread application of generative adversarial networks (GANs) in image generation and content creation, their model architectures and training outcomes have become valuable intellectual property assets. However, in practical deployment, i...
Adversarial attacks involve malicious actors introducing intentional perturbations to machine learning (ML) models, causing unintended behavior. This poses a significant threat to the integrity and trustworthiness of ML models, necessitating the deve...
Over the past decade, Convolutional Neural Networks (CNNs) have been extensively deployed in security-critical areas; however, the security of CNN models is threatened by adversarial attacks. Decision-based adversarial attacks, wherein an attacker re...
Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack. In practical applications, classification models are typically unknown black-box mode...
The remarkably developed graph neural networks (GNNs) are extensively applied to specific tasks in online social networks (OSNs), especially in the vital domain of social trust. Meanwhile, the vulnerability of GNN applied in trust assessment can be e...
DNNs are highly vulnerable to adversarial examples (AEs). To achieve high transferability, traditional AEs often introduce unnatural artifacts that are easily perceptible to the human eye. Unrestricted attacks have emerged as a promising paradigm to...
Network robustness is of paramount importance. Although great progress has been achieved in robustness optimization using single measures, such networks may still be vulnerable to many attack scenarios. Consequently, multi-objective network robustnes...
Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and...
This study improves existing protection strategies for image processing models by embedding invisible watermarks into model outputs to verify the sources of images. Most current methods rely on CNN-based architectures, which are limited by their loca...
Deep neural networks are vulnerable to adversarial examples, even in hard-label black-box settings where only the top-1 prediction is available. To address the challenges of high-dimensional optimization under limited query budgets, we propose two qu...
Adversarial examples reveal critical vulnerabilities in deep neural networks, posing significant risks in real-world deployment. In black-box settings, transferable targeted attacks rely on surrogate models but often suffer from low success rates. We...
Adversarial examples pose a significant threat to Deep Neural Networks (DNNs) underpinning Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems, as these models exhibit acute susceptibility to such malicious inputs. While white-b...
LiDAR point clouds are widely used in remote sensing perception scenarios, such as autonomous driving. However, LiDAR-based perception models remain vulnerable to adversarial perturbations, which may compromise the reliability of safety-critical 3D p...
Spiking Neural Networks (SNNs) have attracted increasing attention due to their energy efficiency and suitability for neuromorphic data processing. Despite these advantages, the security of SNNs—particularly their robustness against backdoor at...
Machine learning models deployed for credit card fraud detection operate in adversarial, security-critical settings, and their robustness against evasion attacks directly affects financial and operational risk. However, despite extensive work on mode...
To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoi...
Adversarial attacks have been extensively studied in recent years to investigate the vulnerability mechanisms of deep neural networks and enhance model robustness and security. However, the transferability of adversarial examples across different mod...
27 November 2023
Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image...
As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial...
In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which...
Numerical simulations employing the dynamic mesh method were performed to investigate the unsteady aerodynamics of variable-sweep wings during morphing. Quasi-steady and unsteady aerodynamic characteristics were compared, and the effects of key opera...
This paper proposes a novel method for transferable adversarial attacks from Image Quality Assessment (IQA) to Video Quality Assessment (VQA) models. Attacking modern VQA models is challenging due to their high complexity and the temporal nature of v...
Wind loading on a transmission tower structure is jointly influenced by the wind field, structural parameters, and the geo-spatial configuration of the transmission line. Considering the multi-parametric effect, this paper aims at developing a limit...
Twin-box decks are being extensively used in the design of long-span bridges due to their superior flutter performance. Although the significant role played by the gap distance has been previously addressed in the frame of experimental studies, there...
Recent research has revealed that the deep neural network (DNN)-based synthetic-aperture radar (SAR) automatic target recognition (ATR) techniques are vulnerable to adversarial examples, which poses significant security risks for their deployment in...
Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in whic...
Adversarial examples have revealed the vulnerability of deep neural networks, and their transferability makes black-box attacks particularly concerning. However, perturbations crafted on a surrogate model often do not remain sufficiently effective on...
AI-driven network intrusion detection systems (NIDSs) have improved the detection of sophisticated and previously unseen attacks. However, the adoption of machine learning also introduces new attack surfaces, most notably backdoor attacks, in which a...
In recent years, unmanned aerial vehicles (UAVs) vision systems based on deep neural networks (DNNs) have made remarkable advancements, demonstrating impressive performance. However, due to the inherent characteristics of DNNs, these systems have bec...
In the rapidly evolving landscape of cybersecurity, model extraction attacks pose a significant challenge, undermining the integrity of machine learning models by enabling adversaries to replicate proprietary algorithms without direct access. This pa...
The aerodynamic performance of compressor stators critically affects aircraft engine efficiency, yet traditional CFD-based evaluation and optimization suffer from high computational cost. This study addresses this gap by developing deep learning surr...
In recent years, with the rapid development of technology, artificial intelligence (AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models a...
In today’s digital era, cyberattacks are rapidly evolving, rendering traditional security mechanisms increasingly inadequate. The adoption of AI-based Network Intrusion Detection Systems (NIDS) has emerged as a promising solution, due to their...
With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating every...
Ultra-high-voltage (UHV) transmission lines are prone to galloping and oscillations under ice and wind loads, posing risks to system reliability and safety. Accurate aerodynamic coefficients are essential for evaluating these effects, but conventiona...
Adversarial transferability makes black-box attacks practical and exposes weaknesses of deep neural networks for computer vision, image recognition, and visual understanding. Among various transferability-enhancing methods, input transformation is on...
The highly nonlinear aerodynamic interference in tandem-airfoil configurations significantly hinders the precise exploitation of their aerodynamic potential. To address this issue, this study establishes a high-fidelity computational fluid dynamics b...
The control and adjustment of in-flight attitudes are critical to enlarging the flight distance of ski jumping. As one of the most important gears, the skis provide sufficient lift and drag forces for the athletes, and thus their in-flight attitudes...
Point cloud perception is important in autonomous driving, robotics, and other security-critical 3D systems, yet learned point cloud classifiers remain vulnerable to transferable adversarial perturbations. A central difficulty in transfer-based black...
Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-...
No-reference image quality assessment (NR-IQA) models achieve high correlation with human mean opinion scores (MOS) on clean benchmarks, yet recent work shows they can be highly vulnerable to small adversarial perturbations that severely degrade rank...
To investigate the aerodynamic characteristics and multi-objective optimization of the variable camber airfoils, the influence of leading- and trailing-edge deflections on aerodynamic performance is conducted. A novel prediction model is presented us...
Mobile networks have become targets of sophisticated cyber attacks. Critical vulnerabilities persist in the General Packet Radio Service Tunneling Protocol (GTP). Signature-based Intrusion Detection Systems (IDS) are inadequate against zero day explo...
The development of sustainable and efficient aircraft concepts, such as those featuring flared folding wing tips (FWTs), introduces both aerodynamic and structural challenges. FWTs have demonstrated strong potential for enhancing aerodynamic performa...
This study develops a tri-level adversarial robust optimization framework for cyber–physical scheduling in smart grids, addressing the intertwined challenges of coordinated cyberattacks, defensive resource allocation, and stochastic operational...
Although a large number of compounds have been identified with antiviral activity against orthopoxviruses in tissue culture systems, it is highly preferred that these compounds have activity in vivo before they can be seriously considered for further...
In the wake of the terrorist attacks of 11 September 2001, extensive research efforts have been dedicated to the development of computational algorithms for identifying contamination sources in water distribution systems (WDSs). Previous studies have...
Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory imp...
Evaluating the adversarial robustness of deployed deep neural networks is critical for trustworthy AI, yet existing black-box assessment tools assume homogeneous input domains shared between surrogates and targets, an assumption rarely met in practic...
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