Author name cluster

Xiaoxiao Hu

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5 papers

2 author rows

ECAI Conference 2025 Conference Paper

Diffusion Model Is a Good Steganalyzer: Magnifying Subtle Perturbations in Image Data

Xiaoxiao Hu
Jiaqi Jin
Shengjiu Dai
Sheng Li 0006
Xinpeng Zhang 0001
Zhenxing Qian

Digital steganography embeds secret messages into images via invisible modifications, posing challenges for steganalysis, which seeks to detect these alterations by analyzing subtle shifts in image distributions. Previous steganalysis efforts primarily focus on enhancing the steganographic signal while suppressing image semantic content, such as through high-pass filtering. However, these empirically designed methods often lack theoretical underpinnings, exhibiting reduced detection accuracy, particularly at low embedding capacities. To address these limitations, we propose an innovative steganalysis approach that transforms images into pure Gaussian noise representations, actively amplifying the subtle distribution shifts introduced by the steganographic processes in spatial images. This paper pioneers the application of diffusion models to magnify steganographic signals, proposing a new paradigm for further research. Specifically, we iteratively perform forward steps of the probability flow in diffusion models to diminish semantic information. By utilizing the natural spreading properties of the diffusion process, we have theoretically validated the efficacy of each forward step in amplifying differences in noise patterns between cover and stego samples. These magnified differences can be easily captured by a simple classifier—a two-layer MLP. Extensive experiments demonstrate the effectiveness of our method, highlighting detection accuracy gains of 10% to 20% under standard conditions and an average 6. 7% increase at low embedding rates compared to existing schemes.

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AAAI Conference 2025 Conference Paper

ScreenMark: Watermarking Arbitrary Visual Content on Screen

Xiujian Liang
Gaozhi Liu
Yichao Si
Xiaoxiao Hu
Zhenxing Qian

Digital watermarking has shown its effectiveness in protecting multimedia content. However, existing watermarking is predominantly tailored for specific media types, rendering them less effective for the protection of content displayed on computer screens, which is often multi-modal and dynamic. Visual Screen Content (VSC), is particularly susceptible to theft and leakage through screenshots, a vulnerability that current watermarking methods fail to adequately address. To address these challenges, we propose ScreenMark, a robust and practical watermarking method designed specifically for arbitrary VSC protection. ScreenMark utilizes a three-stage progressive watermarking framework. Initially, inspired by diffusion principles, we initialize the mutual transformation between regular watermark information and irregular watermark patterns. Subsequently, these patterns are integrated with screen content using a pre-multiplication alpha blending technique, supported by a pre-trained screen decoder for accurate watermark retrieval. The progressively complex distorter enhances the robustness of the watermark in real-world screenshot scenarios. Finally, the model undergoes fine-tuning guided by a joint-level distorter to ensure optimal performance. To validate the effectiveness of ScreenMark, we compiled a dataset comprising 100,000 screenshots from various devices and resolutions. Extensive experiments on different datasets confirm the superior robustness, imperceptibility, and practical applicability of the method.

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ECAI Conference 2025 Conference Paper

SyncGuard: Robust Audio Watermarking Capable of Countering Desynchronization Attacks

Zhenliang Gan
Xiaoxiao Hu
Sheng Li 0006
Zhenxing Qian
Xinpeng Zhang 0001

Audio watermarking has been widely applied in copyright protection and source tracing. However, due to the inherent characteristics of audio signals, watermark localization and resistance to desynchronization attacks remain significant challenges. In this paper, we propose a learning-based scheme named SyncGuard to address these challenges. Specifically, we design a frame-wise broadcast embedding strategy to embed the watermark in arbitrary-length audio, enhancing time-independence and eliminating the need for localization during watermark extraction. To further enhance robustness, we introduce a meticulously designed distortion layer. Additionally, we employ dilated residual blocks in conjunction with dilated gated blocks to effectively capture multi-resolution time-frequency features. Extensive experimental results show that SyncGuard efficiently handles variable-length audio segments, outperforms state-of-the-art methods in robustness against various attacks, and delivers superior auditory quality.

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AAAI Conference 2023 Conference Paper

Bootstrapping Multi-View Representations for Fake News Detection

Qichao Ying
Xiaoxiao Hu
Yangming Zhou
Zhenxing Qian
Dan Zeng
Shiming Ge

Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fidelity of news and how features from different modalities affect the decision-making are still open questions. This paper presents a novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection. Given a multi-modal news, we extract representations respectively from the views of the text, the image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert networks (iMMoE) are proposed for feature refinement and fusion. Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency. With the prediction scores, we reweigh each view of the representations and bootstrap them for fake news detection. Extensive experiments conducted on typical fake news detection datasets prove that BMR outperforms state-of-the-art schemes.

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YNICL Journal 2019 Journal Article

Characteristic alteration of subcortical nuclei shape in medication-free patients with obsessive-compulsive disorder

Lianqing Zhang
Xinyu Hu
Hailong Li
Lu Lu
Bin Li
Xiaoxiao Hu
Xuan Bu
Shi Tang

BACKGROUND: Subcortical nuclei are important components in the pathology model of obsessive-compulsive disorder (OCD), and subregions of these structures subserve different functions that may distinctively contribute to OCD symptoms. Exploration of the subregional-level profile of structural abnormalities of these nuclei is needed to develop a better understanding of the neural mechanism of OCD. METHODS: A total of 83 medication-free, non-comorbid OCD patients and 93 age- and sex-matched healthy controls were recruited, and high-resolution T1-weighted MR images were obtained for all participants. The volume and shape of the subcortical nuclei (including the nucleus accumbens, amygdala, caudate, pallidum, putamen and thalamus) were quantified and compared with an automated parcellation approach and vertex-wise shape analysis using FSL-FIRST software. Sex differences in these measurements were also explored with an exploratory subgroup analysis. RESULTS: Volumetric analysis showed no significant differences between patients and healthy control subjects. Relative to healthy control subjects, the OCD patients showed an expansion of the lateral amygdala (right hemisphere) and right pallidum. These deformities were associated with illness duration and symptom severity of OCD. Exploratory subgroup analysis by sex revealed amygdala deformity in male patients and caudate deformity in female patients. CONCLUSIONS: The lateral amygdala and the dorsal pallidum were associated with OCD. Neuroanatomic evidence of sexual dimorphism was also found in OCD. Our study not only provides deeper insight into how these structures contribute to OCD symptoms by revealing these subregional-level deformities but also suggests that gender effects may be important in OCD studies.

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