Seung-Min Mun

426 total citations
10 papers, 288 citations indexed

About

Seung-Min Mun is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Organic Chemistry. According to data from OpenAlex, Seung-Min Mun has authored 10 papers receiving a total of 288 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Organic Chemistry. Recurrent topics in Seung-Min Mun's work include Digital Media Forensic Detection (8 papers), Advanced Steganography and Watermarking Techniques (5 papers) and Advanced Image Processing Techniques (2 papers). Seung-Min Mun is often cited by papers focused on Digital Media Forensic Detection (8 papers), Advanced Steganography and Watermarking Techniques (5 papers) and Advanced Image Processing Techniques (2 papers). Seung-Min Mun collaborates with scholars based in South Korea. Seung-Min Mun's co-authors include Heung-Kyu Lee, Haneol Jang, Dongkyu Kim, Seung-Hun Nam, Sunghee Choi, Jong‐Uk Hou, In-Jae Yu, Dongkyu Kim and Jinseok Park and has published in prestigious journals such as IEEE Access, Sensors and Neurocomputing.

In The Last Decade

Seung-Min Mun

10 papers receiving 268 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Seung-Min Mun South Korea	9	266	49	38	28	14	10	288
Haneol Jang South Korea	8	260 1.0×	57 1.2×	29 0.8×	32 1.1×	13 0.9×	21	285
Chiou-Ting Hsu Taiwan	8	414 1.6×	38 0.8×	93 2.4×	17 0.6×	26 1.9×	16	426
Shuren Qi China	9	223 0.8×	13 0.3×	62 1.6×	35 1.3×	9 0.6×	21	263
Azadeh Mansouri Iran	10	340 1.3×	86 1.8×	82 2.2×	26 0.9×	4 0.3×	45	353
Junlin Ouyang China	8	300 1.1×	26 0.5×	47 1.2×	10 0.4×	16 1.1×	22	317
Guanshuo Xu United States	4	585 2.2×	20 0.4×	33 0.9×	91 3.3×	19 1.4×	4	600
Beste Üstübioğlu Türkiye	11	304 1.1×	78 1.6×	97 2.6×	68 2.4×	22 1.6×	35	356
Nicolò Bonettini Italy	8	389 1.5×	27 0.6×	46 1.2×	103 3.7×	27 1.9×	14	433
Mohsen Jenadeleh Germany	8	315 1.2×	44 0.9×	71 1.9×	7 0.3×	2 0.1×	16	350
Feng Cheng China	8	136 0.5×	92 1.9×	28 0.7×	52 1.9×	9 0.6×	15	218

Countries citing papers authored by Seung-Min Mun

Since Specialization

Citations

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

Fields of papers citing papers by Seung-Min Mun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seung-Min Mun. 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 Seung-Min Mun. The network helps show where Seung-Min Mun may publish in the future.

Co-authorship network of co-authors of Seung-Min Mun

This figure shows the co-authorship network connecting the top 25 collaborators of Seung-Min Mun. A scholar is included among the top collaborators of Seung-Min Mun 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 Seung-Min Mun. Seung-Min Mun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Mun, Seung-Min, et al.. (2020). Convolutional Neural Network Architecture for Recovering Watermark Synchronization. Sensors. 20(18). 5427–5427. 8 indexed citations

2.

Nam, Seung-Hun, et al.. (2020). NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images. IEEE Access. 8. 93760–93781. 11 indexed citations

3.

Mun, Seung-Min, Seung-Hun Nam, Haneol Jang, Dongkyu Kim, & Heung-Kyu Lee. (2019). Finding robust domain from attacks: A learning framework for blind watermarking. Neurocomputing. 337. 191–202. 112 indexed citations

4.

Nam, Seung-Hun, Seung-Min Mun, Jinseok Park, et al.. (2019). Content-Aware Image Resizing Detection Using Deep Neural Network. 106–110. 13 indexed citations

5.

Jang, Haneol, et al.. (2017). Detecting composite image manipulation based on deep neural networks. 1–5. 22 indexed citations

6.

Jang, Haneol, Dongkyu Kim, Seung-Min Mun, Sunghee Choi, & Heung-Kyu Lee. (2017). DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks. IEEE Signal Processing Letters. 24(12). 1808–1812. 33 indexed citations

7.

Kim, Dongkyu, Haneol Jang, Seung-Min Mun, Sunghee Choi, & Heung-Kyu Lee. (2017). Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks. IEEE Signal Processing Letters. 25(2). 278–282. 54 indexed citations

8.

Nam, Seung-Hun, et al.. (2017). A SIFT features based blind watermarking for DIBR 3D images. Multimedia Tools and Applications. 77(7). 7811–7850. 24 indexed citations

9.

Mun, Seung-Min, et al.. (2015). A robust 3D mesh watermarking scheme against cropping. 1–6. 9 indexed citations

10.

Mun, Seung-Min, et al.. (1997). A Study on Damage Detection of Cutting Tool Using Neural Network and Cutting Force Signal. Journal of the Korean Society for Precision Engineering. 14(12). 48–55. 2 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.

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