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.

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…

This paper identifies and explores a new type of physical adversarial attack on vision systems called Scratch-induced Lens Adversarial Streak Hijacking (SLASH), which causes persistent and selective o…

This paper systematically investigates the vulnerability of near-field mmWave imaging to physical waveform-domain adversarial attacks, demonstrating that while deep learning algorithms show higher rob…

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.

The paper introduces a novel adversarial attack that uses static, view-dependent camouflage on a vehicle to induce consistent feature drift, causing autonomous systems to predict false, yet plausible,…

The paper provides a holistic threat model for LLM-enabled robotic systems by analyzing how conventional, adversarial, and conversational threats propagate across the entire perception-planning-actuat…

This paper investigates acoustic attacks on Artificial Intelligence (AI) based computer vision systems using lower frequencies in the audible range, and explores the impact on various image and object…

This paper tests the impact of acoustic factors on voice control systems and introduces a Dual-Form Signal to Noise Ratio to decouple source stealth from attack efficacy.

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…

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…

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…

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…

The paper formally proves a theorem regarding adversarial noise amplification and proposes a novel, lightweight detection mechanism that uses this enhanced signal for robust adversarial defense.

This paper investigates a novel vulnerability in tactile sensing by demonstrating that targeted Electromagnetic Interference (EMI) can induce strong, misleading 'phantom forces' in Hall-effect fingert…

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 proposes LARAR, a novel layer-wise adaptive regularization approach that enhances the adversarial robustness of neural network-based Network Intrusion Detection Systems by analyzing and miti…

The paper constructs a large, adversarial malware dataset from real-world binaries, demonstrating high evasion rates and showing that even small amounts of poisoned data can severely compromise malwar…

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 introduces AdvScene, a novel scene-grounded framework that measures the real-world 'scene robustness' of adversarial patches by characterizing their operational envelope across varying viewp…