The paper proposes a novel Adversarial Attenuation Patch (AAP) method, which is a physically realizable and stealthy adversarial attack designed to degrade SAR target detection performance.

mmFHE introduces the first system enabling end-to-end mmWave radar sensing using fully homomorphic encryption (FHE), allowing sensitive data processing on untrusted cloud infrastructure while maintain…

This paper proposes a physical backdoor attack against deep learning modulation classifiers, utilizing power amplifier non-linear distortions as physical triggers to achieve high attack success rates.

This paper demonstrates that neural operators used in digital twins for nuclear systems are highly vulnerable to undetectable, sparse adversarial perturbations, necessitating new robustness guarantees…

This paper investigates a novel physical backdoor attack against Deep Automatic Modulation Classifiers (AMC) in wireless communications, demonstrating that an adversary using Explainable AI (XAI) can…

The paper introduces the Street-legal Physical Adversarial Rim (SPAR), a physically realizable and street-legal white-box attack that significantly degrades the accuracy of modern Automatic License Pl…

This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…

The paper introduces Devilray, a comprehensive adversarial model that systematically tests the realistic operational space of fake base stations, revealing significant blind spots in existing detectio…

The paper demonstrates a coordinated, cross-modal spoofing attack that successfully deceives state-of-the-art multi-sensor fusion systems in autonomous vehicles by making multiple sensors agree on a f…

The paper introduces BFIAttack, a novel attack that exploits Beamforming Feedback Information (BFI) to reconstruct a user's Channel State Information (CSI), thereby compromising Wi-Fi physical-layer s…

The paper introduces MoCo-EA, an evolutionary attack method that replaces standard crossover with a continuous Bézier curve interpolation to efficiently exploit the connected manifold structure of adv…

This paper evaluates the physical transfer of adversarial patches against aerial vehicle detectors, finding that while digitally optimized patches can be highly effective, their real-world robustness…

The paper introduces Dummy-Aware Weighted Attack (DAWA), a novel evaluation method that significantly reduces the reported robustness of Dummy Classes-based defenses by simultaneously targeting both t…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

GaMi is a multimodal material identification system that uses mmWave and acoustic sensing with a cross-modal subtractive disentanglement framework to achieve high accuracy (95.2%) for material identif…

The paper introduces a novel Clean-Referenced Feature-Vocoder Attack, a black-box adversarial attack that perturbs high-level SSL feature representations instead of raw audio waveforms, achieving supe…

The paper demonstrates that simpler, shallower Deep Neural Network architectures with reduced features and ReLU activations can inherently improve the robustness of ML-NIDS against gradient-based adve…

This paper addresses the security vulnerability of OFDM-based Physical Layer Authentication (PLA) when channel fading exhibits correlation, proposing a new attack model and a measurable guideline to d…

This paper analyzes the latency-accuracy trade-offs of various TinyML models for detecting diverse cyber-RF threats on autonomous spacecraft, finding that Logistic Regression offers an effective, low-…