Kejiang Chen
Impact in
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- Advanced Steganography and Watermarking Techniques
- Digital Media Forensic Detection
- Chaos-based Image/Signal Encryption
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Advanced Steganography and Watermarking Techniques 59
- Digital Media Forensic Detection 44
- Chaos-based Image/Signal Encryption 29
- Generative Adversarial Networks and Image Synthesis 16
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- Computer Graphics and Visualization Techniques 5
In The Last Decade
Kejiang Chen
73 papers receiving 948 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 747
- Computer Graphics and Computer-Aided Design 59
- Artificial Intelligence 341
- Signal Processing 94
- Media Technology 36
Countries citing papers authored by Kejiang Chen
This map shows the geographic impact of Kejiang Chen'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 Kejiang Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kejiang Chen more than expected).
Fields of papers citing papers by Kejiang Chen
This network shows the impact of papers produced by Kejiang Chen. 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 Kejiang Chen. The network helps show where Kejiang Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kejiang Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 3 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 5 | |
| 13 | 2023 | 12 | |
| 14 | 2023 | 1 | |
| 15 | 2023 | 3 | |
| 16 | 2022 | 6 | |
| 17 | 2022 | 9 | |
| 18 | 2022 | 37 | |
| 19 | Deflecting 3D Adversarial Point Clouds Through Outlier-Guided Removal. | 2018 | 2 |
| 20 | When Provably Secure Steganography Meets Generative Models | 2018 | 5 |
About Kejiang Chen
Kejiang Chen is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Signal Processing, Artificial Intelligence and Information Systems, having authored 84 papers that have together received 965 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (59 papers), Digital Media Forensic Detection (44 papers), Chaos-based Image/Signal Encryption (29 papers), Generative Adversarial Networks and Image Synthesis (16 papers), Adversarial Robustness in Machine Learning (15 papers), Internet Traffic Analysis and Secure E-voting (9 papers), Computer Graphics and Visualization Techniques (5 papers) and Anomaly Detection Techniques and Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (747 citations), Computer Graphics and Computer-Aided Design (59 citations), Artificial Intelligence (341 citations), Signal Processing (94 citations) and Media Technology (36 citations). Kejiang Chen has collaborated with scholars based in China, Singapore and Canada. Frequent co-authors include Weiming Zhang, Nenghai Yu, Hang Zhou, Yaofei Wang, Han Fang, Weixiang Li, Wenbo Zhou, Dongdong Chen, Dongdong Hou and Yujia Liu. Their work appears in journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology and Signal Processing.
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.