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

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 proposes CAAP, a capture-aware adversarial patch framework, demonstrating that deep palmprint recognition systems remain vulnerable to physically realizable attacks despite existing defenses…

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.

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…

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 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…

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…

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 demonstrates that soft fusion in multi-warden covert communication has structural limits, showing that the Fusion Center gains no significant detection advantage from randomizing the number…

The paper introduces SADBench, a systematic benchmark designed to evaluate both the effectiveness of steganographic attacks injecting harmful content and the robustness of steganalysis defenses agains…

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…

This paper proposes using cyber deception with honey drones (HDs) to defend UAV mission systems against Denial-of-Service (DoS) attacks, achieving superior performance using a novel Hypergame-Theoreti…

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…

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 proposes a unified, architecture-agnostic framework that significantly improves the robustness of deepfake image detectors against adversarial attacks by focusing on higher-order frequency s…

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 proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

The paper identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…