Myung-Joon Kwon

527 total citations
12 papers, 288 citations indexed

About

Myung-Joon Kwon is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Myung-Joon Kwon has authored 12 papers receiving a total of 288 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Media Technology. Recurrent topics in Myung-Joon Kwon's work include Digital Media Forensic Detection (7 papers), Advanced Steganography and Watermarking Techniques (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Myung-Joon Kwon is often cited by papers focused on Digital Media Forensic Detection (7 papers), Advanced Steganography and Watermarking Techniques (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Myung-Joon Kwon collaborates with scholars based in South Korea. Myung-Joon Kwon's co-authors include In-Jae Yu, Seung-Hun Nam, Heung-Kyu Lee, Changick Kim, Junyoung Byun and Wonjun Lee and has published in prestigious journals such as IEEE Access, International Journal of Computer Vision and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Myung-Joon Kwon

12 papers receiving 279 citations

Peers

Myung-Joon Kwon

CVPR

AI

MT

BIOPH

LAW

In-Jae Yu South Korea

Nabil A. Lashin Egypt

Nor Bakiah Abd Warif Malaysia

Rudy Becarelli Italy

Qingxiao Guan China

Shuren Qi China

Nicolò Bonettini Italy

Massimo Iuliani Italy

Sushila Maheshkar India

Dijana Tralić Croatia

In-Jae Yu South Korea

Myung-Joon Kwon

291 ×1.3

CVPR

58 ×0.6

AI

98 ×1.2

MT

35 ×1.3

BIOPH

35 ×1.3

LAW

Citations per year, relative to Myung-Joon Kwon Myung-Joon Kwon (= 1×) peers In-Jae Yu

Countries citing papers authored by Myung-Joon Kwon

Since Specialization

Citations

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

Fields of papers citing papers by Myung-Joon Kwon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Myung-Joon Kwon. 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 Myung-Joon Kwon. The network helps show where Myung-Joon Kwon may publish in the future.

Co-authorship network of co-authors of Myung-Joon Kwon

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

All Works

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12 of 12 papers shown

1.

Kwon, Myung-Joon, et al.. (2025). SAFIRE: Segment Any Forged Image Region. Proceedings of the AAAI Conference on Artificial Intelligence. 39(4). 4437–4445. 1 indexed citations

2.

Byun, Junyoung, et al.. (2023). Introducing Competition to Boost the Transferability of Targeted Adversarial Examples Through Clean Feature Mixup. 24648–24657. 7 indexed citations

3.

Kwon, Myung-Joon, et al.. (2023). Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models. 4302–4311. 6 indexed citations

4.

Nam, Seung-Hun, et al.. (2022). DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms. IEEE Multimedia. 29(2). 11–22. 4 indexed citations

5.

Nam, Seung-Hun, et al.. (2022). Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features. Forensic Science International. 340. 111442–111442. 10 indexed citations

6.

Kwon, Myung-Joon, Seung-Hun Nam, In-Jae Yu, Heung-Kyu Lee, & Changick Kim. (2022). Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization. International Journal of Computer Vision. 130(8). 1875–1895. 75 indexed citations

7.

Kwon, Myung-Joon, et al.. (2022). Adaptive Warping Network for Transferable Adversarial Attacks. 2022 IEEE International Conference on Image Processing (ICIP). 3056–3060. 1 indexed citations

8.

Byun, Junyoung, et al.. (2022). Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15223–15232. 47 indexed citations

9.

Kwon, Myung-Joon, In-Jae Yu, Seung-Hun Nam, & Heung-Kyu Lee. (2021). CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing. 375–384. 101 indexed citations

10.

Nam, Seung-Hun, et al.. (2021). Dual-path convolutional neural network for classifying fine-grained manipulations in H.264 videos. Multimedia Tools and Applications. 80(20). 30879–30906. 4 indexed citations

11.

Nam, Seung-Hun, et al.. (2020). Deep Convolutional Neural Network for Identifying Seam-Carving Forgery. IEEE Transactions on Circuits and Systems for Video Technology. 31(8). 3308–3326. 18 indexed citations

12.

Yu, In-Jae, et al.. (2020). Manipulation Classification for JPEG Images Using Multi-Domain Features. IEEE Access. 8. 210837–210854. 14 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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