Feng Ding

1.6k citations
49 papers · 1.1k · 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
    • Medical Image Segmentation Techniques 3
    • Adversarial Robustness in Machine Learning 5

Feng Ding

46 papers receiving 1.0k citations

Peers

Feng Ding
Comparison fields: 5 of 101
  • Computer Vision and Pattern Recognition 525
  • Media Technology 104
  • Artificial Intelligence 310
  • Signal Processing 90
  • Information Systems 164
Replace Lianbing Deng with:
Lianbing Deng China
Zan Gao China
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Farhan Amin South Korea
Qiao Ke China
Asadollah Shahbahrami Iran
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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 2021105
2 202090
3 202175
4 202173
5 202273
6 202350
7 202342
8 202239
9 201438
10 202137
11 202235
12 202134
13 202433
14 202230
15 202027
16 202225
17 201725
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 1.1k 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), Medical Image Segmentation Techniques (3 papers) and Image Processing Techniques and Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (525 citations), Media Technology (104 citations), Artificial Intelligence (310 citations), Signal Processing (90 citations) and Information Systems (164 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, IEEE Transactions on Intelligent Transportation Systems, Information Sciences, Journal of Visual Communication and Image Representation and ACM Transactions on Multimedia Computing Communications and Applications.

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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