arXiv subjects

Youqian Zhang

Publications and source records attributed to Youqian Zhang.

16 recordsLinked to original sources

arXiv2026

Lightweight Detection of Electromagnetic Signal Injection Attacks on Image Sensors

Electromagnetic signal injection attacks (ESIA) pose a growing threat to image sensors, which are increasingly used in different intelligent systems. By emitting electromagnetic interference, adversaries can manipulate pixel values, potentially misleading downstream artificial intelligence (AI) models and causing unsafe decisions in these systems. We present a lightweight detection method that leverages optically black pixels, which are non-exposed pixels already present in many modern image sensors, to identify the attacks. Our detection approach achieves an area under the receiver operating characteristic curve (ROC-AUC) of up to 99.6\% and an Equal Error Rate (EER) as low as 0.027 across diverse attack conditions. Our method requires minimal computational overhead and no hardware modifications, making it a practical and effective defense for securing vision-based systems against ESIA.

cs.CR↗

arXiv2026

A Simulation Framework for Electromagnetic Signal Injection Attacks on Image Sensors

Image sensors are fundamental to many intelligent systems, allowing visual perception and AI-driven decision-making. However, their integrity can be compromised by electromagnetic signal injection attacks (ESIA), which manipulate captured images without modifying sensor hardware or software. Despite the growing threat, system-level understanding of the attacks, as well as the development of defenses, remains limited, in part because collecting adversarial data is often complex and requires specialized attack setups. To address this challenge, we model ESIA and develop a simulation framework for generating synthetic adversarial images. Our analysis shows that these synthetic images are statistically indistinguishable from those produced by real attacks. The proposed framework enables faster vulnerability evaluation of computer vision (CV) algorithms, without the need for dedicated attack hardware. We also present a pilot study showing that the robustness of the algorithms can be improved by adversarial training, demonstrating a practical and scalable path toward mitigating ESIA threats.

cs.CR↗

arXiv2026

Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams

Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource languages. In low-resource settings such as Turkish, detection is especially difficult, as annotated data is scarce and technological defenses remain limited. This research investigates how large language models (LLMs) can support scam detection in Turkish by introducing the first public multi-modal dataset of 100 aligned audio-transcript pairs of scam and benign conversations. We evaluate seven LLMs spanning three model families: Gemini 2.5 (Flash, Flash-Lite, Pro), GPT-4o, and Qwen (Max, Plus, Turbo), under three input conditions: raw audio, automatic speech-to-text transcripts, and transcripts refined by a native speaker. Our results suggest that transcript-based inputs consistently outperform direct audio processing, while human-corrected and uncorrected transcripts perform comparably. By centering a low-resource language and real world threat, this work highlights the urgent need for culturally and linguistically inclusive AI safety research and more robust multi-modal systems for fraud prevention.

cs.CL↗

arXiv2026

Grounding-Driven Attack: Improving Encoder-based Adversarial Transferability against Large Vision-Language Models

Large vision-language models (LVLMs) have achieved impressive performance across multimodal tasks, but their reliance on visual inputs exposes them to adversarial threats. Encoder-based attacks provide an efficient alternative to end-to-end optimization by crafting perturbations through the vision encoder alone. However, existing encoder-based attacks often assume that the surrogate encoder is identical or similar to the victim LVLM's vision encoder. In this work, we present a systematic study of their transferability in more realistic black-box deployments with heterogeneous LVLM architectures. We find that model-specific visual evidence is inconsistent across models, whereas text-conditioned grounding regions are more closely tied to caption-relevant evidence and provide a more stable transfer target. However, existing attacks remain weakly aligned with and insufficiently disrupt these regions. Motivated by these findings, we propose Grounding-Driven Attack (GDA), which aligns perturbation optimization with text-grounded evidence. GDA combines Grounding-Aware Perturbation Allocation to concentrate perturbation budget on grounded evidence regions with Grounding-Centric Evidence Disruption to intensify their global and local disruption. Experiments across diverse victim models and tasks show that GDA consistently outperforms existing encoder-based attacks in black-box transfer. These results highlight the central role of text-grounded evidence in adversarial transferability and motivate grounding-aware robustness evaluation and defense design.

cs.CR↗

arXiv2026

AI Security Research Should Better Incentivize Defense Research

This work examines an imbalance in artificial intelligence (AI) security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech recognition, membership inference, large language models, etc. The imbalance possibly means far beyond a simple count: attack papers are routinely evaluated under favorable conditions that make threats look more severe than they are in practice, while defenses are held to a stricter standard that few can meet. The result is a literature rich in demonstrated vulnerabilities and thin on usable and deployed protections. We thus argue that AI security research should better incentivize defense research.

cs.CR↗

arXiv2026

Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI

Embodied intelligent robots rely on tactile sensors to interact with the physical world safely. While the security of visual perception systems has been studied (e.g., adversarial samples), the integrity of the tactile sensory channel remains unexplored. This work explores a vulnerability in Hall-effect fingertip sensors, showing their susceptibility to intentional Electromagnetic Interference (EMI). We demonstrate that a targeted signal injection can induce strong "phantom forces", amplifying perceived force magnitude by over 9$\times$ and deviating the inferred force direction by 65$^\circ$. Such perturbations can paralyze learning-based tactile classification models, seriously affecting robot movement. An attacker could exploit this vulnerability to coerce a robot hand into crushing fragile objects or dropping dangerous payloads.

cs.CR↗

arXiv2025

One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detection

Phone scams remain a pervasive threat to both personal safety and financial security worldwide. Recent advances in large language models (LLMs) have demonstrated strong potential in detecting fraudulent behavior by analyzing transcribed phone conversations. However, these capabilities introduce notable privacy risks, as such conversations frequently contain sensitive personal information that may be exposed to third-party service providers during processing. In this work, we explore how to harness LLMs for phone scam detection while preserving user privacy. We propose MASK (Modular Adaptive Sanitization Kit), a trainable and extensible framework that enables dynamic privacy adjustment based on individual preferences. MASK provides a pluggable architecture that accommodates diverse sanitization methods - from traditional keyword-based techniques for high-privacy users to sophisticated neural approaches for those prioritizing accuracy. We also discuss potential modeling approaches and loss function designs for future development, enabling the creation of truly personalized, privacy-aware LLM-based detection systems that balance user trust and detection effectiveness, even beyond phone scam context.

cs.CR↗

arXiv2025

Rainbow Artifacts from Electromagnetic Signal Injection Attacks on Image Sensors

Image sensors are integral to a wide range of safety- and security-critical systems, including surveillance infrastructure, autonomous vehicles, and industrial automation. These systems rely on the integrity of visual data to make decisions. In this work, we investigate a novel class of electromagnetic signal injection attacks that target the analog domain of image sensors, allowing adversaries to manipulate raw visual inputs without triggering conventional digital integrity checks. We uncover a previously undocumented attack phenomenon on CMOS image sensors: rainbow-like color artifacts induced in images captured by image sensors through carefully tuned electromagnetic interference. We further evaluate the impact of these attacks on state-of-the-art object detection models, showing that the injected artifacts propagate through the image signal processing pipeline and lead to significant mispredictions. Our findings highlight a critical and underexplored vulnerability in the visual perception stack, highlighting the need for more robust defenses against physical-layer attacks in such systems.

cs.CR↗

arXiv2025

"It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams

Despite living in the era of the internet, phone-based scams remain one of the most prevalent forms of scams. These scams aim to exploit victims for financial gain, causing both monetary losses and psychological distress. While governments, industries, and academia have actively introduced various countermeasures, scammers also continue to evolve their tactics, making phone scams a persistent threat. To combat these increasingly sophisticated scams, detection technologies must also advance. In this work, we propose a framework for modeling scam calls and introduce an LLM-based real-time detection approach, which assesses fraudulent intent in conversations, further providing immediate warnings to users to mitigate harm. Through experiments, we evaluate the method's performance and analyze key factors influencing its effectiveness. This analysis enables us to refine the method to improve precision while exploring the trade-off between recall and timeliness, paving the way for future directions in this critical area of research.

cs.HC↗

arXiv2025

Is Your Autonomous Vehicle Safe? Understanding the Threat of Electromagnetic Signal Injection Attacks on Traffic Scene Perception

Autonomous vehicles rely on camera-based perception systems to comprehend their driving environment and make crucial decisions, thereby ensuring vehicles to steer safely. However, a significant threat known as Electromagnetic Signal Injection Attacks (ESIA) can distort the images captured by these cameras, leading to incorrect AI decisions and potentially compromising the safety of autonomous vehicles. Despite the serious implications of ESIA, there is limited understanding of its impacts on the robustness of AI models across various and complex driving scenarios. To address this gap, our research analyzes the performance of different models under ESIA, revealing their vulnerabilities to the attacks. Moreover, due to the challenges in obtaining real-world attack data, we develop a novel ESIA simulation method and generate a simulated attack dataset for different driving scenarios. Our research provides a comprehensive simulation and evaluation framework, aiming to enhance the development of more robust AI models and secure intelligent systems, ultimately contributing to the advancement of safer and more reliable technology across various fields.

cs.CR↗

arXiv2024

Combating Phone Scams with LLM-based Detection: Where Do We Stand?

Phone scams pose a significant threat to individuals and communities, causing substantial financial losses and emotional distress. Despite ongoing efforts to combat these scams, scammers continue to adapt and refine their tactics, making it imperative to explore innovative countermeasures. This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. By analyzing the conversational dynamics between scammers and victims, LLM-based detectors can identify potential scams as they occur, offering immediate protection to users. While such approaches demonstrate promising results, we also acknowledge the challenges of biased datasets, relatively low recall, and hallucinations that must be addressed for further advancement in this field

cs.CR↗

arXiv2024

Anti-ESIA: Analyzing and Mitigating Impacts of Electromagnetic Signal Injection Attacks

Cameras are integral components of many critical intelligent systems. However, a growing threat, known as Electromagnetic Signal Injection Attacks (ESIA), poses a significant risk to these systems, where ESIA enables attackers to remotely manipulate images captured by cameras, potentially leading to malicious actions and catastrophic consequences. Despite the severity of this threat, the underlying reasons for ESIA's effectiveness remain poorly understood, and effective countermeasures are lacking. This paper aims to address these gaps by investigating ESIA from two distinct aspects: pixel loss and color strips. By analyzing these aspects separately on image classification tasks, we gain a deeper understanding of how ESIA can compromise intelligent systems. Additionally, we explore a lightweight solution to mitigate the effects of ESIA while acknowledging its limitations. Our findings provide valuable insights for future research and development in the field of camera security and intelligent systems.

cs.CR↗

arXiv2024

Understanding Impacts of Electromagnetic Signal Injection Attacks on Object Detection

Object detection can localize and identify objects in images, and it is extensively employed in critical multimedia applications such as security surveillance and autonomous driving. Despite the success of existing object detection models, they are often evaluated in ideal scenarios where captured images guarantee the accurate and complete representation of the detecting scenes. However, images captured by image sensors may be affected by different factors in real applications, including cyber-physical attacks. In particular, attackers can exploit hardware properties within the systems to inject electromagnetic interference so as to manipulate the images. Such attacks can cause noisy or incomplete information about the captured scene, leading to incorrect detection results, potentially granting attackers malicious control over critical functions of the systems. This paper presents a research work that comprehensively quantifies and analyzes the impacts of such attacks on state-of-the-art object detection models in practice. It also sheds light on the underlying reasons for the incorrect detection outcomes.

cs.CR↗

arXiv2022

Electromagnetic Signal Injection Attacks on Differential Signaling

Differential signaling is a method of data transmission that uses two complementary electrical signals to encode information. This allows a receiver to reject any noise by looking at the difference between the two signals, assuming the noise affects both signals in the same way. Many protocols such as USB, Ethernet, and HDMI use differential signaling to achieve a robust communication channel in a noisy environment. This generally works well and has led many to believe that it is infeasible to remotely inject attacking signals into such a differential pair. In this paper we challenge this assumption and show that an adversary can in fact inject malicious signals from a distance, purely using common-mode injection, i.e., injecting into both wires at the same time. We show how this allows an attacker to inject bits or even arbitrary messages into a communication line. Such an attack is a significant threat to many applications, from home security and privacy to automotive systems, critical infrastructure, or implantable medical devices; in which incorrect data or unauthorized control could cause significant damage, or even fatal accidents. We show in detail the principles of how an electromagnetic signal can bypass the noise rejection of differential signaling, and eventually result in incorrect bits in the receiver. We show how an attacker can exploit this to achieve a successful injection of an arbitrary bit, and we analyze the success rate of injecting longer arbitrary messages. We demonstrate the attack on a real system and show that the success rate can reach as high as $90\%$. Finally, we present a case study where we wirelessly inject a message into a Controller Area Network (CAN) bus, which is a differential signaling bus protocol used in many critical applications, including the automotive and aviation sector.

cs.CR↗

arXiv2022

Detection of Electromagnetic Signal Injection Attacks on Actuator Systems

An actuator is a device that converts electricity into another form of energy, typically physical movement. They are absolutely essential for any system that needs to impact or modify the physical world, and are used in millions of systems of all sizes, all over the world, from cars and spacecraft to factory control systems and critical infrastructure. An actuator is a "dumb device" that is entirely controlled by the surrounding electronics, e.g., a microcontroller, and thus cannot authenticate its control signals or do any other form of processing. The problem we look at in this paper is how the wires that connect an actuator to its control electronics can act like antennas, picking up electromagnetic signals from the environment. This makes it possible for a remote attacker to wirelessly inject signals (energy) into these wires to bypass the controller and directly control the actuator. To detect such attacks, we propose a novel detection method that allows the microcontroller to monitor the control signal and detect attacks as a deviation from the intended value. We have managed to do this without requiring the microcontroller to sample the signal at a high rate or run any signal processing. That makes our defense mechanism practical and easy to integrate into existing systems. Our method is general and applies to any type of actuator (provided a few basic assumptions are met), and can deal with adversaries with arbitrarily high transmission power. We implement our detection method on two different practical systems to show its generality, effectiveness, and robustness.

cs.CR↗

arXiv2019

A Framework for Evaluating Security in the Presence of Signal Injection Attacks

Sensors are embedded in security-critical applications from medical devices to nuclear power plants, but their outputs can be spoofed through electromagnetic and other types of signals transmitted by attackers at a distance. To address the lack of a unifying framework for evaluating the effects of such transmissions, we introduce a system and threat model for signal injection attacks. We further define the concepts of existential, selective, and universal security, which address attacker goals from mere disruptions of the sensor readings to precise waveform injections. Moreover, we introduce an algorithm which allows circuit designers to concretely calculate the security level of real systems. Finally, we apply our definitions and algorithm in practice using measurements of injections against a smartphone microphone, and analyze the demodulation characteristics of commercial Analog-to-Digital Converters (ADCs). Overall, our work highlights the importance of evaluating the susceptibility of systems against signal injection attacks, and introduces both the terminology and the methodology to do so.

cs.CR↗