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Zihang Zou

dblp:302/3705 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0009-0155-0112ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Digital forensics and information hiding · 60% Security and privacy of machine learning · 40%
Artificial intelligence
2 papers
Generative modeling · 84% Trustworthy machine learning · 16%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images! · ICCV 2025
Digital forensics and information hiding
copyright protection
0.912025
Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images! · ICCV 2025
Security and privacy of machine learning › model intellectual property protection › model watermarking
deep neural network watermarking
0.612022
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks · ECCV (13) 2022
Machine learning › Trustworthy machine learning › model security
model watermarking
0.212022
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks · ECCV (13) 2022

Methods — techniques the papers use, named apart from their topics

membership inference · 1.7diffusion model · 1.7watermarking · 1.1neuron perturbation · 1.1
YearPublicationVenuePosition
2026 Journey of the Glazed Dragon Tiles: Designing Processual Storytelling for Museum Artifacts in Virtual Reality
abstract
Museums often present cultural artifacts as static objects with textual descriptions, limiting understanding of historical change over time. This paper presents a VR experience that explores processual storytelling for cultural artifacts through spatial-temporal progression and embodied interaction. Focusing on the Glazed Dragon Tiles from the British Museum, we designed an immersive narrative that reconstructs the artifact's historical trajectory across interconnected scenes, including a temple, transportation, and auction context. We conducted a qualitative user study with 15 participants in a university setting in London. Findings show that participants developed a processual understanding of the artifact's history through movement across scenes and changing environmental and material conditions. Embodied interactions further supported engagement by positioning participants within the narrative structure. This work contributes a process-oriented approach to VR museum storytelling and design insights into integrating spatial-temporal progression and embodied interaction in cultural heritage experiences.
Fengyihan Wu, Zixuan Ma, Zihang Zou, Jinquan Ruan
Creativity & Cognition3
2025 Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!
Zihang Zou, Boqing Gong, Liqiang Wang 0001
ICCV1
2022 Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks
Zihang Zou, Boqing Gong, Liqiang Wang 0001
ECCV (13)1