Feng Ding

1.5k citations
49 papers · 1.0k · h-index 20

Impact in

Papers in

    • Digital Media Forensic Detection 19
    • Generative Adversarial Networks and Image Synthesis 15
    • Advanced Steganography and Watermarking Techniques 11
    • Advanced Image Processing Techniques 8
    • Image Enhancement Techniques 4
    • Adversarial Robustness in Machine Learning 5

Feng Ding

44 papers receiving 992 citations

Peers

Feng Ding
Comparison fields: 5 of 102
  • Computer Vision and Pattern Recognition 508
  • Media Technology 94
  • Artificial Intelligence 310
  • Signal Processing 89
  • Information Systems 161
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Citations per field
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Citations per year

Countries citing papers authored by Feng Ding

Since Specialization
Citations

This map shows the geographic impact of Feng Ding'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 Feng Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng Ding more than expected).

Fields of papers citing papers by Feng Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng Ding. 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 Feng Ding. The network helps show where Feng Ding may publish in the future.

Co-authors

The 25 scholars most cited alongside Feng Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Feng Ding Line = papers co-authored together Feng Ding links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021101
2 202089
3 202174
4 202271
5 202171
6 202349
7 202342
8 202237
9 201437
10 202135
11 202134
12 202234
13 202230
14 202027
15 201725
16 202224
17 202424
18 201923
19 201722
20 201419

About Feng Ding

Feng Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Information Systems and Media Technology, having authored 49 papers that have together received 1.0k indexed citations. Recurring topics across this work include Digital Media Forensic Detection (19 papers), Generative Adversarial Networks and Image Synthesis (15 papers), Advanced Steganography and Watermarking Techniques (11 papers), Advanced Image Processing Techniques (8 papers), Adversarial Robustness in Machine Learning (5 papers), Image Enhancement Techniques (4 papers), Image Processing Techniques and Applications (3 papers) and Digital and Cyber Forensics (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (508 citations), Media Technology (94 citations), Artificial Intelligence (310 citations), Signal Processing (89 citations) and Information Systems (161 citations). Feng Ding has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Keping Yu, Guopu Zhu, Zhiwei Guo, Yun-Qing Shi, Xiangjun Li, Alireza Jolfaei, Zhangyi Shen, Siwei Lyu, Pradeep K. Atrey and Mamoun Alazab. Their work appears in journals such as IEEE Signal Processing Letters, Information Sciences, IEEE Transactions on Intelligent Transportation Systems, Signal Processing Image Communication and IEEE Transactions on Circuits and Systems for Video Technology.

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.

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