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Hanqing Guo

Publications and source records attributed to Hanqing Guo.

At least 19 recordsLinked to original sources
arXiv2026

SweepMark: Query-Free and High-Fidelity Attacks on Robust Audio Watermarking

Audio watermarking embeds imperceptible signals into speech to trace misuse in text-to-speech (TTS) synthesis. Existing removal attacks may retain watermark-related artifacts or degrade speech quality when suppressing them. We propose SweepMark, a query-free black-box attack that targets these artifacts without access to target watermarking models or paired clean and watermarked samples. Our key insight is that watermark embedding introduces spectral patterns that are not fully aligned with natural speech, including persistent structures that conventional speech enhancement may retain. Diverse Artifact Learning extracts complementary stationary and non-stationary patterns, using customized spectral artifacts added to public speech to learn persistent structures without target watermark examples. Adaptive Artifact Scaling then combines and amplifies these patterns for subtraction while constraining distortion. Across two speech datasets and four watermarking methods, SweepMark achieves average attack success rates of 92% and 96%, with higher perceptual quality scores overall than the evaluated adaptive attacks. Automatic speech recognition evaluation indicates that linguistic content is largely preserved. Further experiments with three TTS systems show high attack success against two synthesis-resistant watermarking methods in speech generated after fine-tuning on attacked recordings.

cs.CR↗
arXiv2026

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Transferable attacks allow adversarial manipulations or learned attack mechanisms constructed in one setting to compromise other AI systems, potentially reducing attack costs and amplifying security and privacy risks. Yet fragmented terminology and evaluation practices hinder understanding of what transfers, why it transfers, and where transfer fails. This survey synthesizes representative research and develops a unified taxonomy spanning six complementary axes: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-deployment. By separating source-target configuration changes from attacker access and adaptation budgets, the taxonomy provides a common framework for comparing transfer across evasion, poisoning, backdoors, privacy inference, model stealing, and prompt-based attacks. We connect transfer mechanisms, including gradient alignment, shared representations, training persistence, and deployment invariances, to enhancement methods based on augmentation, disentanglement, and optimization. Our synthesis identifies inconsistent transfer, incompatible metrics, and gaps between simulated and physical evaluations, and establishes a research agenda for compositional transfer, configuration differences, temporal persistence, and reproducible benchmarking. Together, this work supports systematic investigation of shared vulnerabilities and guides transfer-aware security assessment and defense.

cs.CR↗
arXiv2026

Forging LLM Authorship Fingerprints with Targeted Rewriting

Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. Accurate attribution on unmodified text, however, does not show whether the prediction still identifies the original source after deliberate rewriting. We formulate this problem as targeted fingerprint transfer: rewriting one model's output so that attribution classifiers assign it to a chosen target model. We study summarization, where different models receive the same document and express the same underlying content, providing a controlled setting for conditional generation. We introduce ForgePrint, a search-then-distil framework that first searches for rewrites that move attribution toward a target fingerprint, then distils the selected rewrites into a one-pass 4B Student model. On CNN/DM, the Student reaches 70.2% target success rate, outperforming both its Teacher (54.1%) and the strongest of six published rewriting baselines (39.3%), against held-out classifiers that are never queried by the attack. It also reaches 68.3% target success when transferring summaries from an open model toward chosen commercial models. These results show that fingerprint detectability should not be conflated with source authenticity, and that text-only attribution can provide misleading evidence of model identity under targeted rewriting, even when it is accurate on unmodified text.

cs.CL↗
arXiv2026

ActReal: System-Level Mobile Agents Challenge Mobile Automation Detection

System-level mobile agents are evolving from fixed scripts into adaptive systems that continuously observe interfaces, reason, and adjust their actions, allowing automated attacks to navigate dynamic UIs and complete complex tasks. Existing applications detect automation using touch trajectories, action timing, and the physical coupling between touch and inertial measurement unit (IMU) signals. However, a privileged system-level agent executor can control both touchscreen input and application-visible sensor delivery, enabling it to jointly generate time-aligned touch and six-axis IMU signals and evade these defenses. We present ActReal, a physical-action attack framework for system-level mobile agents. ActReal converts semantic agent actions into task-valid touch and IMU events using genuine-trajectory adaptation and physics-guided IMU generation. ActReal achieves a mean event-level attack success rate of 77.5\%; even when detectors jointly observe touch and IMU, its attack success rate remains 71.1\%.

cs.CR↗
arXiv2026

A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents

Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning. However, their increasing autonomy also introduces a new attack surface: adversarial interactions can manipulate agent behavior through direct prompt injection, indirect content attacks, and multi-turn escalation strategies. Existing defense strategies focus on prompt-level filtering and rule-based guardrails, which are often insufficient when risk emerges gradually across interaction sequences. In this work, we propose a complementary defense mechanism: a low-latency fraud detection layer for detecting adversarial interaction patterns in LLM-powered agents. Instead of determining whether a single prompt is malicious, our approach models risk over interaction trajectories using structured runtime features derived from prompt characteristics, session dynamics, tool usage, execution context, and fraud-inspired signals. The detection layer can be implemented using lightweight models leading to low-latency real-time deployments. To evaluate the framework, we construct a synthetic corpus of 12,000 multi-turn agent interactions generated from parameterized templates that simulate realistic agentic workflows. Using 42 structured features and an XGBoost classifier, our detector achieves over 9 times faster than LLM-based detectors. Through the experiment and ablation studies, our work suggests that interaction-level behavioral detection should become a core component of deployment-time defense for LLM-powered agents.

cs.AI↗
arXiv2026

Learning to Evade: Adaptive Attacks on Audio Watermarking

Advances in generative audio have intensified copyright concerns, making audio watermarking increasingly important for asserting ownership. However, existing audio watermarking methods are vulnerable to adversarial attacks. We find that watermark decoder message probabilities follow normal distributions, a property exploited by defenses to detect manipulations. This paper introduces an adaptive audio watermark attack method (AWM) designed to bypass existing defense strategies. AWM uses a two-stage optimization: the first stage ensures attack success, while the second improves audio quality. To evade detection, it estimates normal distribution parameters from limited samples of the target audio, and then adaptively steers decoded probabilities back into the estimated range. Evaluated on two watermarking methods across three voice datasets, AWM achieves high success while bypassing state-of-the-art detectors: detection rates are below 10% for replacement and creation, and 0% for removal.

cs.SD↗
arXiv2026

Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors

With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. However, adversaries may try to remove these watermarks, making it critical to evaluate how well watermarking schemes withstand removal attacks. Existing attacks are often impractical: they either noticeably degrade perceptual quality or require access to the watermarking scheme. We propose DiffErase, a black-box watermark removal attack that assumes no knowledge of the target watermarking scheme while maintaining perceptual quality. DiffErase perturbs watermarked audio to an intermediate diffusion noise level and regenerates it using a pretrained denoising model, effectively suppressing watermark signals. Theoretical analysis and extensive experiments demonstrate that inaudible audio watermarks are highly vulnerable: across multiple audio domains, DiffErase consistently removes watermarks while preserving perceptual quality. These findings highlight the need for future audio watermarking designs to consider diffusion-based threats. Code and demos are available at https://differase.github.io/DiffErase/.

cs.CR↗
arXiv2026

Mental Damage: Caption Poisoning Attacks on Retrieval-Augmented Text-to-Music Generation

Retrieval-augmented text-to-music (TTM) systems augment underspecified user prompts using captions retrieved from a music caption dataset. This design introduces an integrity dependency on the music knowledge database. We show that an attacker can poison the database by injecting a small number of crafted music captions, causing the system to retrieve malicious captions that bias prompt augmentation and steer generation away from the user's intended function, without modifying the user prompt, retriever, or generator. To achieve the music caption poisoning attack, we propose a dual-layer caption poisoning strategy that preserves high-level retrieval anchors while injecting low-level acoustic descriptors to steer prompt augmentation and downstream music generation toward an attacker-chosen target intent. In a MusicCaps knowledge database, CLAP retriever, and MusicGen pipeline, poisoned generations move substantially closer to the attacker's target, while remaining comparably aligned with the original user query. These results expose a practical integrity risk for retrieval-augmented creative AI systems. Our demo can be found at: https://yizhu-wen.github.io/Mental-Damage/

cs.SD↗
arXiv2026

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool and generation. We analyze the search engine optimization (SEO) to GEO transition to identify two risks: (i) concentrated influence from low contestability and system sensitivity, and (ii) undisclosed commercial influence embedded in evidence and reasoning. We then formalize a general GEO pipeline to locate where optimization acts and compare academic and industry practices, revealing a third risk: (iii) academic-industry blind spots driven by visibility and evaluation asymmetries between offline setups and deployed systems. This position argues the need for answer-level governance and measurement: stronger contestability, high-precision disclosure, black-box auditing of material influence, and deployment-aligned metrics for exposure persistence.

cs.CY↗
arXiv2025

A Survey on Reconfigurable Intelligent Surfaces in Practical Systems: Security and Privacy Perspectives

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. While extensive research has explored their theoretical benefits for communication and sensing, practical deployments in smart environments such as homes, vehicles, and industrial settings remain limited and under-examined, particularly from security and privacy perspectives. This survey provides a comprehensive examination of RIS applications in real-world systems, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and demonstrate how RIS may introduce new attacks to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as applying additional security algorithms, disrupting attackers, and early detection of unauthorized RIS. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also provide a collection of open-source tools, datasets, demos, and papers at: https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.

cs.CR↗
arXiv2025

Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

cs.CR↗
arXiv2025

ClearMask: Noise-Free and Naturalness-Preserving Protection Against Voice Deepfake Attacks

Voice deepfake attacks, which artificially impersonate human speech for malicious purposes, have emerged as a severe threat. Existing defenses typically inject noise into human speech to compromise voice encoders in speech synthesis models. However, these methods degrade audio quality and require prior knowledge of the attack approaches, limiting their effectiveness in diverse scenarios. Moreover, real-time audios, such as speech in virtual meetings and voice messages, are still exposed to voice deepfake threats. To overcome these limitations, we propose ClearMask, a noise-free defense mechanism against voice deepfake attacks. Unlike traditional approaches, ClearMask modifies the audio mel-spectrogram by selectively filtering certain frequencies, inducing a transferable voice feature loss without injecting noise. We then apply audio style transfer to further deceive voice decoders while preserving perceived sound quality. Finally, optimized reverberation is introduced to disrupt the output of voice generation models without affecting the naturalness of the speech. Additionally, we develop LiveMask to protect streaming speech in real-time through a universal frequency filter and reverberation generator. Our experimental results show that ClearMask and LiveMask effectively prevent voice deepfake attacks from deceiving speaker verification models and human listeners, even for unseen voice synthesis models and black-box API services. Furthermore, ClearMask demonstrates resilience against adaptive attackers who attempt to recover the original audio signal from the protected speech samples.

cs.SD↗
arXiv2025

Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models

The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AI-generated content. In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. Our method follows principled motivations and effectively addresses an intractable black-box optimization problem. Real-world experiments demonstrated that our methods improve defense success rates from 2.5% to 88.6% on different LLMs, outperforming traditional defenses such as configuration-based restrictions.

cs.CR↗
arXiv2025

SpeechVerifier: Robust Acoustic Fingerprint against Tampering Attacks via Watermarking

With the surge of social media, maliciously tampered public speeches, especially those from influential figures, have seriously affected social stability and public trust. Existing speech tampering detection methods remain insufficient: they either rely on external reference data or fail to be both sensitive to attacks and robust to benign operations, such as compression and resampling. To tackle these challenges, we introduce SpeechVerifer to proactively verify speech integrity using only the published speech itself, i.e., without requiring any external references. Inspired by audio fingerprinting and watermarking, SpeechVerifier can (i) effectively detect tampering attacks, (ii) be robust to benign operations and (iii) verify the integrity only based on published speeches. Briefly, SpeechVerifier utilizes multiscale feature extraction to capture speech features across different temporal resolutions. Then, it employs contrastive learning to generate fingerprints that can detect modifications at varying granularities. These fingerprints are designed to be robust to benign operations, but exhibit significant changes when malicious tampering occurs. To enable speech verification in a self-contained manner, the generated fingerprints are then embedded into the speech signal by segment-wise watermarking. Without external references, SpeechVerifier can retrieve the fingerprint from the published audio and check it with the embedded watermark to verify the integrity of the speech. Extensive experimental results demonstrate that the proposed SpeechVerifier is effective in detecting tampering attacks and robust to benign operations.

cs.CR↗
arXiv2025

Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework

Recent advances in Multimodal Large Language Models (MLLMs) have significantly enhanced the naturalness and flexibility of human computer interaction by enabling seamless understanding across text, vision, and audio modalities. Among these, voice enabled models such as SpeechGPT have demonstrated considerable improvements in usability, offering expressive, and emotionally responsive interactions that foster deeper connections in real world communication scenarios. However, the use of voice introduces new security risks, as attackers can exploit the unique characteristics of spoken language, such as timing, pronunciation variability, and speech to text translation, to craft inputs that bypass defenses in ways not seen in text-based systems. Despite substantial research on text based jailbreaks, the voice modality remains largely underexplored in terms of both attack strategies and defense mechanisms. In this work, we present an adversarial attack targeting the speech input of aligned MLLMs in a white box scenario. Specifically, we introduce a novel token level attack that leverages access to the model's speech tokenization to generate adversarial token sequences. These sequences are then synthesized into audio prompts, which effectively bypass alignment safeguards and to induce prohibited outputs. Evaluated on SpeechGPT, our approach achieves up to 89 percent attack success rate across multiple restricted tasks, significantly outperforming existing voice based jailbreak methods. Our findings shed light on the vulnerabilities of voice-enabled multimodal systems and to help guide the development of more robust next-generation MLLMs.

cs.CL↗
arXiv2025

SoK: How Robust is Audio Watermarking in Generative AI models?

Audio watermarking is increasingly used to verify the provenance of AI-generated content, enabling applications such as detecting AI-generated speech, protecting music IP, and defending against voice cloning. To be effective, audio watermarks must resist removal attacks that distort signals to evade detection. While many schemes claim robustness, these claims are typically tested in isolation and against a limited set of attacks. A systematic evaluation against diverse removal attacks is lacking, hindering practical deployment. In this paper, we investigate whether recent watermarking schemes that claim robustness can withstand a broad range of removal attacks. First, we introduce a taxonomy covering 22 audio watermarking schemes. Next, we summarize their underlying technologies and potential vulnerabilities. We then present a large-scale empirical study to assess their robustness. To support this, we build an evaluation framework encompassing 22 types of removal attacks (109 configurations) including signal-level, physical-level, and AI-induced distortions. We reproduce 9 watermarking schemes using open-source code, identify 8 new highly effective attacks, and highlight 11 key findings that expose the fundamental limitations of these methods across 3 public datasets. Our results reveal that none of the surveyed schemes can withstand all tested distortions. This evaluation offers a comprehensive view of how current watermarking methods perform under real-world threats. Our demo and code are available at https://sokaudiowm.github.io/.

cs.CR↗
arXiv2025

FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation

Transformer-based foundation models (FMs) have recently demonstrated remarkable performance in medical image segmentation. However, scaling these models is challenging due to the limited size of medical image datasets within isolated hospitals, where data centralization is restricted due to privacy concerns. These constraints, combined with the data-intensive nature of FMs, hinder their broader application. Integrating federated learning (FL) with foundation models (FLFM) fine-tuning offers a potential solution to these challenges by enabling collaborative model training without data sharing, thus allowing FMs to take advantage of a diverse pool of sensitive medical image data across hospitals/clients. However, non-independent and identically distributed (non-IID) data among clients, paired with computational and communication constraints in federated environments, presents an additional challenge that limits further performance improvements and remains inadequately addressed in existing studies. In this work, we propose a novel FLFM fine-tuning framework, \underline{\textbf{Fed}}erated tuning with \underline{\textbf{S}}imilarity-guided \underline{\textbf{C}}ollaborative \underline{\textbf{A}}ggregation (FedSCA), encompassing all phases of the FL process. This includes (1) specially designed parameter-efficient fine-tuning (PEFT) for local client training to enhance computational efficiency; (2) partial low-level adapter transmission for communication efficiency; and (3) similarity-guided collaborative aggregation (SGCA) on the server side to address non-IID issues. Extensive experiments on three FL benchmarks for medical image segmentation demonstrate the effectiveness of our proposed FedSCA, establishing new SOTA performance.

eess.IV↗
arXiv2025

YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion

Vision-based drone-to-drone detection has attracted increasing attention due to its importance in numerous tasks such as vision-based swarming, aerial see-and-avoid, and malicious drone detection. However, existing methods often encounter failures when the background is complex or the target is tiny. This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments using motion guidance. It starts by creating a motion difference map to capture the motion characteristics of tiny drones. Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone. Finally, the fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 frames) covering various challenging conditions and has the smallest average object size compared with the existing drone detection datasets. Extensive experiments on the ARD100 and NPS-Drones datasets show that our proposed detector performs exceptionally well under challenging conditions and surpasses state-of-the-art algorithms across various metrics. We publicly release the codes and ARD100 dataset at https://github.com/Irisky123/YOLOMG.

cs.CV↗