Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
2020899 citationsYuezun Li, Xin Yang et al.profile →
Exposing Deep Fakes Using Inconsistent Head Poses
2019644 citationsXin Yang, Yuezun Li et al.profile →
In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
2018559 citationsYuezun Li, Ming‐Ching Chang et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yuezun Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yuezun Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuezun Li more than expected).
This network shows the impact of papers produced by Yuezun Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yuezun Li. The network helps show where Yuezun Li may publish in the future.
Co-authorship network of co-authors of Yuezun Li
This figure shows the co-authorship network connecting the top 25 collaborators of Yuezun Li.
A scholar is included among the top collaborators of Yuezun Li based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Yuezun Li. Yuezun Li is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Li, Yuezun, Xin Yang, Pu Sun, Honggang Qi, & Siwei Lyu. (2020). Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. 3204–3213.899 indexed citations breakdown →
16.
Li, Yuezun, Xin Yang, Pu Sun, Honggang Qi, & Siwei Lyu. (2019). Celeb-DF: A New Dataset for DeepFake Forensics. arXiv (Cornell University).59 indexed citations
17.
Li, Yuezun, Xiao Bian, Ming‐Ching Chang, & Siwei Lyu. (2019). Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches.. British Machine Vision Conference. 218.5 indexed citations
18.
Li, Yuezun, et al.. (2018). Robust Adversarial Perturbation on Deep Proposal-based Models.. arXiv (Cornell University). 231.7 indexed citations
19.
Li, Yuezun & Siwei Lyu. (2018). Exposing DeepFake Videos By Detecting Face Warping Artifacts. arXiv (Cornell University). 46–52.119 indexed citations
20.
Li, Yuezun, et al.. (2018). Attacking Object Detectors via Imperceptible Patches on Background.. arXiv (Cornell University).5 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.