arXiv subjects

Xiaoyu Ji

Publications and source records attributed to Xiaoyu Ji.

At least 19 recordsLinked to original sources

arXiv2026

"Your Robot Was Trained on a Lie": Collision Mesh Poisoning Attacks on Robotic Manipulation

Learning-enabled robotic manipulation increasingly relies on robot simulators for policy training and evaluation before real-world deployment. Inside a simulator, a 3D asset contains two separate geometries: a visual mesh used for rendering and a collision mesh used for physical interaction. For computational efficiency, the collision mesh is deliberately a coarse approximation that need not have the same geometry as the visual mesh, a legitimate and pervasive discrepancy we call the Visual--Collision Gap (V--C Gap). We show that the V--C Gap opens a new and practical attack surface, and propose Collision Mesh Poisoning (CMP), the first poisoning attack against robotic manipulation delivered through the 3D asset supply chain. An attacker modifies only the collision mesh of a 3D asset, leaving the visual mesh and all other components unchanged. A policy trained and evaluated with the poisoned asset behaves normally throughout simulation, yet degrades, fails, or creates physical safety risks once deployed in the real world. Since current asset review practices cover malware, copyright, and format compliance, but not visual--collision consistency, poisoned assets can be distributed through legitimate supply chain channels. We evaluate several defenses and our results show that they are insufficient to defend against CMP, highlighting the need for new defenses.

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arXiv2026

Learning Human-Like Badminton Skills for Humanoid Robots

Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world.

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arXiv2026

GhostTac: Manipulating Tactile Sensors without Physical Contact

Tactile sensors are integral components of modern robotic systems, enabling robots to perceive and interact with the physical environment through tactile feedback. Despite their importance, the physical-layer security of tactile sensors has received little attention in prior work. In this paper, we present GhostTac, to the best of our knowledge, the first contactless attack that manipulates tactile sensing via electromagnetic interference (EMI). We identify that EMI exploits the nonlinear rectification and limited bandwidth amplification effects, allowing carefully crafted EMI signals to be converted into a persistent DC offset that bypasses on-board filtering and induces stable measurement deviations. Building on this mechanism, GhostTac enables fine-grained and controllable manipulation of sensor outputs by reshaping the spatial distribution and manipulating the magnitude at the targeted location. Such interference can induce unintended and harmful robot behaviors, such as causing a domestic robot to exert excessive force, resulting in physical damage or human injury. We evaluate GhostTac on 10 sensor modules and 2 dexterous hands, covering 15 tactile sensors of different types, and demonstrate consistent attack effectiveness across all tested devices. We further present three case studies on tactile grasping, slip detection, and material classification to illustrate practical impacts in real robotic tasks. We envision that our findings shed light on a new physical attack vector against tactile sensing in robotic systems.

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arXiv2026

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches

By integrating Chain-of-Thought (CoT) reasoning, Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, particularly by improving generalization and interpretability. However, the security of CoT-based reasoning mechanisms remains largely unexplored. In this paper, we show that CoT reasoning introduces a novel attack vector for targeted behavior hijacking--for example, causing a robot to mistakenly deliver a knife to a person instead of an apple--without modifying the user's instruction. We first provide empirical evidence that CoT strongly governs action generation, even when it is semantically misaligned with the input instructions. Building on this observation, we propose TRAP, the first targeted behavior-hijacking adversarial attack against CoT-reasoning VLA models. By targeting the reasoning-to-action pathway, TRAP uses an adversarial patch (e.g., a tablecloth placed on the table) to steer intermediate CoT reasoning and downstream actions toward adversary-defined behaviors. Extensive evaluations on three representative reasoning VLAs, spanning distinct CoT reasoning mechanisms, demonstrate the effectiveness of TRAP. Notably, we implemented the patch by printing it on paper in a real-world setting. Our findings highlight the urgent need to secure CoT reasoning in VLA systems. The project page is available at https://zhengxian-huang.github.io/TRAP-website/.

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arXiv2026

False Reality: Uncovering Sensor-induced Human-VR Interaction Vulnerability

Virtual Reality (VR) techniques, serving as the bridge between the real and virtual worlds, have boomed and are widely used in manufacturing, remote healthcare, gaming, etc. Specifically, VR systems offer users immersive experiences that include both perceptions and actions. Various studies have demonstrated that attackers can manipulate VR software to influence users' interactions, including perception and actions. However, such attacks typically require strong access and specialized expertise. In this paper, we are the first to present a systematic analysis of physical attacks against VR systems and introduce False Reality, a new attack threat to VR devices without requiring access to or modification of their software. False Reality disturbs VR system services by tampering with sensor measurements, and further spoofing users' perception even inducing harmful actions, e.g., inducing dizziness or causing users to crash into obstacles, by exploiting perceptual and psychological effects. We formalize these threats through an attack pathway framework and validate three representative pathways via physical experiments and user studies on five commercial VR devices. Finally, we further propose a defense prototype to mitigate such threats. Our findings shall provide valuable insights for enhancing the security and resilience of future VR systems.

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arXiv2026

Adaptive Greedy Frame Selection for Long Video Understanding

Large vision--language models (VLMs) are increasingly applied to long-video question answering, yet inference is often bottlenecked by the number of input frames and resulting visual tokens. Naive sparse sampling can miss decisive moments, while purely relevance-driven selection frequently collapses onto near-duplicate frames and sacrifices coverage of temporally distant evidence. We propose a question-adaptive greedy frame selection method that jointly optimizes query relevance and semantic representativeness under a fixed frame budget. Our approach constructs a 1~FPS candidate pool (capped at 1000) with exact timestamp alignment, embeds candidates in two complementary spaces (SigLIP for question relevance and DINOv2 for semantic similarity), and selects frames by greedily maximizing a weighted sum of a modular relevance term and a facility-location coverage term. This objective is normalized, monotone, and submodular, yielding a standard (1-1/e) greedy approximation guarantee. To account for question-dependent trade-offs between relevance and coverage, we introduce four preset strategies and a lightweight text-only question-type classifier that routes each query to its best-performing preset. Experiments on MLVU show consistent accuracy gains over uniform sampling and a strong recent baseline across frame budgets, with the largest improvements under tight budgets.

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arXiv2026

Differentially Private and Communication Efficient Large Language Model Split Inference via Stochastic Quantization and Soft Prompt

Large Language Models (LLMs) have achieved remarkable performance and received significant research interest. The enormous computational demands, however, hinder the local deployment on devices with limited resources. The current prevalent LLM inference paradigms require users to send queries to the service providers for processing, which raises critical privacy concerns. Existing approaches propose to allow the users to obfuscate the token embeddings before transmission and utilize local models for denoising. Nonetheless, transmitting the token embeddings and deploying local models may result in excessive communication and computation overhead, preventing practical implementation. In this work, we propose \textbf{DEL}, a framework for \textbf{D}ifferentially private and communication \textbf{E}fficient \textbf{L}LM split inference. More specifically, an embedding projection module and a differentially private stochastic quantization mechanism are proposed to reduce the communication overhead in a privacy-preserving manner. To eliminate the need for local models, we adapt soft prompt at the server side to compensate for the utility degradation caused by privacy. To the best of our knowledge, this is the first work that utilizes soft prompt to improve the trade-off between privacy and utility in LLM inference, and extensive experiments on text generation and natural language understanding benchmarks demonstrate the effectiveness of the proposed method.

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arXiv2026

Instance camera focus prediction for crystal agglomeration classification

Agglomeration refers to the process of crystal clustering due to interparticle forces. Crystal agglomeration analysis from microscopic images is challenging due to the inherent limitations of two-dimensional imaging. Overlapping crystals may appear connected even when located at different depth layers. Because optical microscopes have a shallow depth of field, crystals that are in-focus and out-of-focus in the same image typically reside on different depth layers and do not constitute true agglomeration. To address this, we first quantified camera focus with an instance camera focus prediction network to predict 2 class focus level that aligns better with visual observations than traditional image processing focus measures. Then an instance segmentation model is combined with the predicted focus level for agglomeration classification. Our proposed method has a higher agglomeration classification and segmentation accuracy than the baseline models on ammonium perchlorate crystal and sugar crystal dataset.

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arXiv2025

Phantom Menace: Exploring and Enhancing the Robustness of VLA Models Against Physical Sensor Attacks

Vision-Language-Action (VLA) models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA models to interpret complex, real-world environments using diverse sensor data streams. Given the fact that VLA-based systems heavily rely on the sensory input, the security of VLA models against physical-world sensor attacks remains critically underexplored. To address this gap, we present the first systematic study of physical sensor attacks against VLAs, quantifying the influence of sensor attacks and investigating the defenses for VLA models. We introduce a novel "Real-Sim-Real" framework that automatically simulates physics-based sensor attack vectors, including six attacks targeting cameras and two targeting microphones, and validates them on real robotic systems. Through large-scale evaluations across various VLA architectures and tasks under varying attack parameters, we demonstrate significant vulnerabilities, with susceptibility patterns that reveal critical dependencies on task types and model designs. We further develop an adversarial-training-based defense that enhances VLA robustness against out-of-distribution physical perturbations caused by sensor attacks while preserving model performance. Our findings expose an urgent need for standardized robustness benchmarks and mitigation strategies to secure VLA deployments in safety-critical environments.

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arXiv2025

Denoising of Two-Phase Optically Sectioned Structured Illumination Reconstructions Using Encoder-Decoder Networks

Structured illumination (SI) enhances image resolution and contrast by projecting patterned light onto a sample. In two-phase optical-sectioning SI (OS-SI), reduced acquisition time introduces residual artifacts that conventional denoising struggles to suppress. Deep learning offers an alternative to traditional methods; however, supervised training is limited by the lack of clean, optically sectioned ground-truth data. We investigate encoder-decoder networks for artifact reduction in two-phase OS-SI, using synthetic training pairs formed by applying real artifact fields to synthetic images. An asymmetrical denoising autoencoder (DAE) and a U-Net are trained on the synthetic data, then evaluated on real OS-SI images. Both networks improve image clarity, with each excelling against different artifact types. These results demonstrate that synthetic training enables supervised denoising of OS-SI images and highlight the potential of encoder-decoder networks to streamline reconstruction workflows.

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arXiv2025

SoK: Understanding the Fundamentals and Implications of Sensor Out-of-band Vulnerabilities

Sensors are fundamental to cyber-physical systems (CPS), enabling perception and control by transducing physical stimuli into digital measurements. However, despite growing research on physical attacks on sensors, our understanding of sensor hardware vulnerabilities remains fragmented due to the ad-hoc nature of this field. Moreover, the infinite attack signal space further complicates threat abstraction and defense. To address this gap, we propose a systematization framework, termed sensor out-of-band (OOB) vulnerabilities, that for the first time provides a comprehensive abstraction for sensor attack surfaces based on underlying physical principles. We adopt a bottom-up systematization methodology that analyzes OOB vulnerabilities across three levels. At the component level, we identify the physical principles and limitations that contribute to OOB vulnerabilities. At the sensor level, we categorize known attacks and evaluate their practicality. At the system level, we analyze how CPS features such as sensor fusion, closed-loop control, and intelligent perception impact the exposure and mitigation of OOB threats. Our findings offer a foundational understanding of sensor hardware security and provide guidance and future directions for sensor designers, security researchers, and system developers aiming to build more secure sensors and CPS.

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arXiv2025

Confidence-aware agglomeration classification and segmentation of 2D microscopic food crystal images

Food crystal agglomeration is a phenomenon occurs during crystallization which traps water between crystals and affects food product quality. Manual annotation of agglomeration in 2D microscopic images is particularly difficult due to the transparency of water bonding and the limited perspective focusing on a single slide of the imaged sample. To address this challenge, we first propose a supervised baseline model to generate segmentation pseudo-labels for the coarsely labeled classification dataset. Next, an instance classification model that simultaneously performs pixel-wise segmentation is trained. Both models are used in the inference stage to combine their respective strengths in classification and segmentation. To preserve crystal properties, a post processing module is designed and included to both steps. Our method improves true positive agglomeration classification accuracy and size distribution predictions compared to other existing methods. Given the variability in confidence levels of manual annotations, our proposed method is evaluated under two confidence levels and successfully classifies potential agglomerated instances.

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arXiv2025

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execute solutions back onto the platform for others to use with privacy and confidentiality guarantees. We illustrate this problem through a particular use case which addresses an important problem in the large-scale manufacturing of food crystals, which is that quality control relies on image analysis tools. Previous to our research, food crystals in the images were manually counted, which required substantial and time-consuming human efforts, but we have developed and deployed a crystal analysis tool which makes this process both more rapid and accurate. The tool enables automatic characterization of the crystal size distribution and numbers from microscope images while the natural imperfections from the sample preparation are automatically removed; a machine learning model to count high resolution translucent crystals and agglomeration of crystals was also developed to aid in these efforts. The resulting algorithm was then packaged for real-world use on the factory floor via a web-based app secured through the originating privacy-preserving platform, allowing manufacturers to use it while keeping their proprietary data secure. After demonstrating this full process, future directions are also explored.

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arXiv2024

PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation

Sensors are key components enabling various applications, e.g., home intrusion detection and environmental monitoring. While various software defenses and physical protections are used to prevent sensor manipulation, this paper introduces a new threat vector, PowerRadio, that bypasses existing protections and changes sensor readings from a distance. PowerRadio leverages interconnected ground (GND) wires, a standard practice for electrical safety at home, to inject malicious signals. The injected signal is coupled by the sensor's analog measurement wire and eventually survives the noise filters, inducing incorrect measurement. We present three methods to manipulate sensors by inducing static bias, periodical signals, or pulses. For instance, we show adding stripes into the captured images of a surveillance camera or injecting inaudible voice commands into conference microphones. We study the underlying principles of PowerRadio and identify its root causes: (1) the lack of shielding between ground and data signal wires and (2) the asymmetry of circuit impedance that enables interference to bypass filtering. We validate PowerRadio against a surveillance system, broadcast systems, and various sensors. We believe that PowerRadio represents an emerging threat, exhibiting the advantages of both radiated and conducted EMI, e.g., expanding the effective attack distance of radiated EMI yet eliminating the requirement of line-of-sight or approaching physically. Our insights shall provide guidance for enhancing the sensors' security and power wiring during the design phases.

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arXiv2024

SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Models

Text-to-image (T2I) models, such as Stable Diffusion, have exhibited remarkable performance in generating high-quality images from text descriptions in recent years. However, text-to-image models may be tricked into generating not-safe-for-work (NSFW) content, particularly in sexually explicit scenarios. Existing countermeasures mostly focus on filtering inappropriate inputs and outputs, or suppressing improper text embeddings, which can block sexually explicit content (e.g., naked) but may still be vulnerable to adversarial prompts -- inputs that appear innocent but are ill-intended. In this paper, we present SafeGen, a framework to mitigate sexual content generation by text-to-image models in a text-agnostic manner. The key idea is to eliminate explicit visual representations from the model regardless of the text input. In this way, the text-to-image model is resistant to adversarial prompts since such unsafe visual representations are obstructed from within. Extensive experiments conducted on four datasets and large-scale user studies demonstrate SafeGen's effectiveness in mitigating sexually explicit content generation while preserving the high-fidelity of benign images. SafeGen outperforms eight state-of-the-art baseline methods and achieves 99.4% sexual content removal performance. Furthermore, our constructed benchmark of adversarial prompts provides a basis for future development and evaluation of anti-NSFW-generation methods.

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arXiv2024

Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control

This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suffers from inconsistency issues. Food crystal segmentation is a challenging problem due to the diverse shapes of crystals and their surrounding hard mimics. To address this challenge, we propose an efficient instance segmentation method based on object detection. Experimental results show that the predicted crystal counting accuracy of our method is comparable with existing segmentation methods, while being five times faster. Based on our experiments, we also define objective criteria for separating hard mimics and food crystals, which could benefit manual annotation tasks on similar dataset.

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arXiv2024

ReThink: Reveal the Threat of Electromagnetic Interference on Power Inverters

With the boom of renewable energy sources (RES), the number of power inverters proliferates. Power inverters are the key electronic devices that transform the direct current (DC) power from RES to the alternating current (AC) power on the grids, and their security can affect the stable operation of RES and even power grids. This paper analyzes the security of photovoltaic (PV) inverters from the aspects of internal sensors since they serve as the foundation for safe power conversion. We discover that both the embedded current sensors and voltage sensors are vulnerable to electromagnetic interference (EMI) of 1 GHz or higher, despite electromagnetic compatibility (EMC) countermeasures. Such vulnerabilities can lead to incorrect measurements and deceiving the control algorithms, and we design ReThink that could produce three types of consequences on PV inverters by emitting carefully crafted EMI, i.e., Denial of Service (DoS), damaging inverters physically or damping the power output. We successfully validate these consequences on 5 off-the-shelf PV inverters, and even in a real-world microgrid, by transmitting EMI signals at a distance of 100-150cm and a total power within 20W. Our work aims to raise awareness of the security of power electronic devices of RES, as they represent an emerging Cyber-Physical attack surface to the future RES-dominated grid. Finally, to cope with such threats, we provide hardware and software-based countermeasures.

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arXiv2024

PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

LiDAR (Light Detection and Ranging) is a pivotal sensor for autonomous driving, offering precise 3D spatial information. Previous signal attacks against LiDAR systems mainly exploit laser signals. In this paper, we investigate the possibility of cross-modality signal injection attacks, i.e., injecting intentional electromagnetic interference (IEMI) to manipulate LiDAR output. Our insight is that the internal modules of a LiDAR, i.e., the laser receiving circuit, the monitoring sensors, and the beam-steering modules, even with strict electromagnetic compatibility (EMC) testing, can still couple with the IEMI attack signals and result in the malfunction of LiDAR systems. Based on the above attack surfaces, we propose the PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off. We evaluate and demonstrate the effectiveness of PhantomLiDAR with both simulated and real-world experiments on five COTS LiDAR systems. We also conduct feasibility experiments in real-world moving scenarios. We provide potential defense measures that can be implemented at both the sensor level and the vehicle system level to mitigate the risks associated with IEMI attacks. Video demonstrations can be viewed at https://sites.google.com/view/phantomlidar.

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