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 introduces a dual-layer side-channel attack framework that exploits the variable workload introduced by dynamic image preprocessing in local Vision-Language Models (VLMs) to infer sensitive…

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 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…

DETOUR proposes a practical backdoor attack against object detection models by using semantic triggers that are robust to variations in size, location, and field of view (FoV), overcoming limitations…

The paper proposes an on-device framework to detect and prevent the forwarding of images that have been physically recaptured (photographed) from a mobile screen, addressing the Screen Recaptured Anal…

The paper introduces a stealthy, scenario-realistic data fabrication attack that subtly manipulates object poses in shared perception data to induce unsafe driving behaviors in connected and autonomou…

ThermalTap presents the first passive, non-contact side-channel attack that fingerprints virtual reality (VR) applications by analyzing the long-wave infrared (LWIR) thermal radiation emitted by the h…

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…

This paper systematically revisits and expands the threat model for backdoor attacks on semantic segmentation, proposing a unified framework (BADSEG) that demonstrates severe, previously overlooked vu…

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,…

This paper introduces FaceTell, a novel side-channel attack system that demonstrates the feasibility of eavesdropping on fine-grained on-screen application activities by analyzing subtle reflections v…

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 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 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…

The paper demonstrates that passive motion traces recorded during a mobile selfie capture can serve as a measurable, low-friction auxiliary signal for enhancing both spoof screening and user identity…

The paper demonstrates that the Brazilian e-Voting Machine interface generates a simple and highly distinctive electromagnetic spectral signature, raising significant concerns about its susceptibility…

The paper introduces Multi-Clip Video (MCV) SafetyBench, a dataset demonstrating that the vulnerability of Multimodal Large Language Models (MLLMs) to jailbreaking increases with the diversity and num…

PatchPoison introduces a lightweight dataset-poisoning method that injects small, high-frequency adversarial patches into multi-view image datasets to systematically corrupt feature matching and degra…

The paper proposes a decentralized, privacy-aware framework enabling smart cameras to autonomously coordinate their view coverage in public spaces while explicitly excluding sensitive regions, achievi…