南京理工大学2022级工业设计专业本科生本科阶段我连续两年综测成绩排名年级第一。我积极努力学习人工智能，目前以一作身份发表CCF-A类会议1篇，CCF-B类会议3篇，SCI期刊2篇，北大核心期刊1篇，并担任包括NeurIPS、IJCAI的多个顶级会议/期刊的审稿人。此外，我拥有2项实用新型专利、1项外观专利，并获得多项国际/国家级奖项。我大学英语六级637分、雅思7分、大学德语四级通过，并在多个场合协助对外籍友人的翻译工作。
研究兴趣人工智能安全，可信大模型，计算机视觉对抗攻击
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南京理工大学设计艺术与传媒学院
本科生2022.09 - present
Honors & Awards
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美国大学生数学建模竞赛M奖2022
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亚太地区数学建模竞赛二等奖2022/2023
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全国大学生英语竞赛全国一等奖2023
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全国大学生机器人竞赛ROBOCON全国一等奖2024
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中西部外语翻译大赛全国一等奖2024
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中国好创意暨全国数字艺术设计大赛全国二等奖2024
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新文科实践创新大赛全国银奖2024
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互联网+创新创业大赛江苏省一、二等奖2024
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全国大学生工业设计大赛江苏省一等奖2024
Service
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2024 IJCAI Main TrackPC成员
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2025 IJCNN Main Track审稿人
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2024 NeurIPS Workshop (SafeGenAi/Compression/FM4Science)审稿人
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2024 ICWSM Main Track审稿人
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Neurocomputing (SCI-II)审稿人
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Applied Intelligence (SCI-II)审稿人
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Pattern Recognition Letters (SCI-III)审稿人
News
Selected Publications (view all )
HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval
Chihan Huang, Xiaobo Shen
AAAI Conference on Artificial Intelligence (AAAI) 2025 Poster
Deep hashing models have achieved great success in retrieval tasks due to their powerful representation and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding subtle, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on retrieval models. We propose HUANG, the first targeted adversarial attack algorithm to leverage a diffusion model for hashing retrieval in black-box scenarios. In our approach, adversarial denoising uses adversarial perturbations and residual image to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.
HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval
Chihan Huang, Xiaobo Shen
AAAI Conference on Artificial Intelligence (AAAI) 2025 Poster
Deep hashing models have achieved great success in retrieval tasks due to their powerful representation and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding subtle, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on retrieval models. We propose HUANG, the first targeted adversarial attack algorithm to leverage a diffusion model for hashing retrieval in black-box scenarios. In our approach, adversarial denoising uses adversarial perturbations and residual image to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.
Efficient Multi-branch Black-box Semantic-aware Targeted Attack Against Deep Hashing Retrieval
Chihan Huang, Xiaobo Shen
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2025 Poster
Deep hashing have achieved exceptional performance in retrieval tasks due to their robust representational capabilities. However, they inherit the vulnerability of deep neural networks to adversarial attacks. These models are susceptible to finely crafted adversarial perturbations that can lead them to return incorrect retrieval results. Although numerous adversarial attack methods have been proposed, there has been a scarcity of research focusing on targeted black-box attacks against deep hashing models. We introduce the Efficient Multi-branch Black-box Semantic-aware Targeted Attack against Deep Hashing Retrieval (EmbSTar), capable of executing targeted black-box attacks on hashing models. Initially, we distill the target model to create a knockoff model. Subsequently, we devised novel Target Fusion and Target Adaptation modules to integrate and enhance the semantic information of the target label and image. Knockoff model is then utilized to align the adversarial image more closely with the target image semantically. With the knockoff model, we can obtain powerful targeted attacks with few queries. Extensive experiments demonstrate that EmbSTar significantly surpasses previous models in its targeted attack capabilities, achieving SOTA performance for targeted black-box attacks.
Efficient Multi-branch Black-box Semantic-aware Targeted Attack Against Deep Hashing Retrieval
Chihan Huang, Xiaobo Shen
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2025 Poster
Deep hashing have achieved exceptional performance in retrieval tasks due to their robust representational capabilities. However, they inherit the vulnerability of deep neural networks to adversarial attacks. These models are susceptible to finely crafted adversarial perturbations that can lead them to return incorrect retrieval results. Although numerous adversarial attack methods have been proposed, there has been a scarcity of research focusing on targeted black-box attacks against deep hashing models. We introduce the Efficient Multi-branch Black-box Semantic-aware Targeted Attack against Deep Hashing Retrieval (EmbSTar), capable of executing targeted black-box attacks on hashing models. Initially, we distill the target model to create a knockoff model. Subsequently, we devised novel Target Fusion and Target Adaptation modules to integrate and enhance the semantic information of the target label and image. Knockoff model is then utilized to align the adversarial image more closely with the target image semantically. With the knockoff model, we can obtain powerful targeted attacks with few queries. Extensive experiments demonstrate that EmbSTar significantly surpasses previous models in its targeted attack capabilities, achieving SOTA performance for targeted black-box attacks.
PoemBERT: A Dynamic Masking Content and Ratio Based Semantic Language Model For Chinese Poem Generation
Chihan Huang, Xiaobo Shen
International Conference on Computational Linguistics (COLING) 2025 Poster
Ancient Chinese poetry stands as a crucial treasure in Chinese culture. To address the absence of pre-trained models for ancient poetry, we introduced PoemBERT, a BERT-based model utilizing a corpus of classical Chinese poetry. Recognizing the unique emotional depth and linguistic precision of poetry, we incorporated sentiment and pinyin embeddings into the model, enhancing its sensitivity to emotional information and addressing challenges posed by the phenomenon of multiple pronunciations for the same Chinese character. Additionally, we proposed Character Importance-based masking and dynamic masking strategies, significantly augmenting the model's capability to extract imagery-related features and handle poetry-specific information. Fine-tuning our PoemBERT model on various downstream tasks, including poem generation and sentiment classification, resulted in state-of-the-art performance in both automatic and manual evaluations. We provided explanations for the selection of the dynamic masking rate strategy and proposed a solution to the issue of a small dataset size.
PoemBERT: A Dynamic Masking Content and Ratio Based Semantic Language Model For Chinese Poem Generation
Chihan Huang, Xiaobo Shen
International Conference on Computational Linguistics (COLING) 2025 Poster
Ancient Chinese poetry stands as a crucial treasure in Chinese culture. To address the absence of pre-trained models for ancient poetry, we introduced PoemBERT, a BERT-based model utilizing a corpus of classical Chinese poetry. Recognizing the unique emotional depth and linguistic precision of poetry, we incorporated sentiment and pinyin embeddings into the model, enhancing its sensitivity to emotional information and addressing challenges posed by the phenomenon of multiple pronunciations for the same Chinese character. Additionally, we proposed Character Importance-based masking and dynamic masking strategies, significantly augmenting the model's capability to extract imagery-related features and handle poetry-specific information. Fine-tuning our PoemBERT model on various downstream tasks, including poem generation and sentiment classification, resulted in state-of-the-art performance in both automatic and manual evaluations. We provided explanations for the selection of the dynamic masking rate strategy and proposed a solution to the issue of a small dataset size.
Progressive Artistic Aesthetic Enhancement For Chinese Ink Painting Style Transfer
Chihan Huang
European Conference on Artificial Intelligence (ECAI) 2024 Poster
The translation of artistic style is a challenging yet crucial task for both computer vision and the arts, and the unique attributes of Chinese ink painting—such as its use of negative space, brushwork, ink diffusion, and more—present significant challenges to the application of existing style transfer algorithms. In response to these distinctive characteristics, we propose a progressive artistic aethetic ink painting style transfer method. The progressive multi-scale aesthetic style attention module in the network leverages the complementary benefits of shallow and deep style information to progressively fuse style features across multiple scales. The covariance transform fusion module addresses issues of stylistic disharmony and enhances the aesthetic quality of the style transfer while preserving the content structure effectively. Additionally, we have developed adaptive spatial interpolation module for detailed information finetuning. Finally, we conducted comparative experiments with previous studies as well as ablation studies, and invited 30 experts in art and design to perform manual evaluations. The results demonstrate that our method can achieve more aesthetically pleasing Chinese ink painting style transfers, confirming its effectiveness and artistic integrity.
Progressive Artistic Aesthetic Enhancement For Chinese Ink Painting Style Transfer
Chihan Huang
European Conference on Artificial Intelligence (ECAI) 2024 Poster
The translation of artistic style is a challenging yet crucial task for both computer vision and the arts, and the unique attributes of Chinese ink painting—such as its use of negative space, brushwork, ink diffusion, and more—present significant challenges to the application of existing style transfer algorithms. In response to these distinctive characteristics, we propose a progressive artistic aethetic ink painting style transfer method. The progressive multi-scale aesthetic style attention module in the network leverages the complementary benefits of shallow and deep style information to progressively fuse style features across multiple scales. The covariance transform fusion module addresses issues of stylistic disharmony and enhances the aesthetic quality of the style transfer while preserving the content structure effectively. Additionally, we have developed adaptive spatial interpolation module for detailed information finetuning. Finally, we conducted comparative experiments with previous studies as well as ablation studies, and invited 30 experts in art and design to perform manual evaluations. The results demonstrate that our method can achieve more aesthetically pleasing Chinese ink painting style transfers, confirming its effectiveness and artistic integrity.