In-Jae Yu

595 total citations
18 papers, 321 citations indexed

About

In-Jae Yu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, In-Jae Yu has authored 18 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 3 papers in Media Technology and 2 papers in Public Health, Environmental and Occupational Health. Recurrent topics in In-Jae Yu's work include Digital Media Forensic Detection (13 papers), Advanced Steganography and Watermarking Techniques (10 papers) and Advanced Image Processing Techniques (4 papers). In-Jae Yu is often cited by papers focused on Digital Media Forensic Detection (13 papers), Advanced Steganography and Watermarking Techniques (10 papers) and Advanced Image Processing Techniques (4 papers). In-Jae Yu collaborates with scholars based in South Korea. In-Jae Yu's co-authors include Heung-Kyu Lee, Seung-Hun Nam, Myung-Joon Kwon, Changick Kim, Jinseok Park, Jin-Seok Park, Dongkyu Kim, Jong‐Uk Hou, Kyungsu Kim and Sunghee Choi and has published in prestigious journals such as IEEE Access, BMC Bioinformatics and International Journal of Computer Vision.

In The Last Decade

In-Jae Yu

18 papers receiving 315 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
In-Jae Yu South Korea	11	291	98	58	35	35	18	321
Myung-Joon Kwon South Korea	7	231 0.8×	82 0.8×	91 1.6×	26 0.7×	28 0.8×	12	288
Nor Bakiah Abd Warif Malaysia	7	266 0.9×	109 1.1×	31 0.5×	44 1.3×	74 2.1×	16	309
A. Costanzo Italy	10	424 1.5×	120 1.2×	47 0.8×	59 1.7×	57 1.6×	19	434
Qingxiao Guan China	12	344 1.2×	92 0.9×	45 0.8×	18 0.5×	33 0.9×	27	359
Dijana Tralić Croatia	8	267 0.9×	133 1.4×	17 0.3×	33 0.9×	69 2.0×	17	295
Nicolò Bonettini Italy	8	389 1.3×	46 0.5×	103 1.8×	27 0.8×	16 0.5×	14	433
Massimo Iuliani Italy	9	402 1.4×	70 0.7×	62 1.1×	85 2.4×	23 0.7×	24	422
Yuzhuo Ren United States	6	305 1.0×	126 1.3×	62 1.1×	35 1.0×	42 1.2×	10	317
Zenan Shi China	9	242 0.8×	83 0.8×	52 0.9×	21 0.6×	41 1.2×	20	273
Sushila Maheshkar India	9	173 0.6×	65 0.7×	35 0.6×	12 0.3×	31 0.9×	23	223

Countries citing papers authored by In-Jae Yu

Since Specialization

Citations

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

Fields of papers citing papers by In-Jae Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by In-Jae Yu. 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 In-Jae Yu. The network helps show where In-Jae Yu may publish in the future.

Co-authorship network of co-authors of In-Jae Yu

This figure shows the co-authorship network connecting the top 25 collaborators of In-Jae Yu. A scholar is included among the top collaborators of In-Jae Yu 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 In-Jae Yu. In-Jae Yu 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:

18 of 18 papers shown

Yu, In-Jae, et al.. (2023). MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation. 614–623. 10 indexed citations

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

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

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

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

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

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

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

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

10.

Jang, Haneol, et al.. (2020). Local-Source Enhanced Residual Network for Steganalysis of Digital Images. IEEE Access. 8. 137789–137798. 6 indexed citations

11.

Lim, Yongsub, et al.. (2019). PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks. BMC Bioinformatics. 20(S13). 381–381. 7 indexed citations

12.

Nam, Seung-Hun, Jinseok Park, Dongkyu Kim, et al.. (2019). Two-Stream Network for Detecting Double Compression of H.264 Videos. 111–115. 17 indexed citations

13.

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

14.

Yu, In-Jae, et al.. (2018). A Study on the Korea Smart City Certification Index and Demonstration Authentication. Journal of the Korea Academia-Industrial cooperation Society. 19(1). 688–698. 2 indexed citations

15.

Yu, In-Jae, et al.. (2018). A Study on the Linkage between Intelligent Security Technology based on Spatial Information and other Technologies for Demonstration of Convergence Technology. Journal of the Korea Academia-Industrial cooperation Society. 19(1). 622–632. 1 indexed citations

16.

Park, Jin-Seok, et al.. (2018). Paired mini-batch training: A new deep network training for image forensics and steganalysis. Signal Processing Image Communication. 67. 132–139. 11 indexed citations

17.

Hou, Jong‐Uk, In-Jae Yu, & Heung-Kyu Lee. (2018). Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code. Applied Sciences. 8(7). 1040–1040. 2 indexed citations

18.

Yu, In-Jae, et al.. (2017). Identifying photorealistic computer graphics using convolutional neural networks. 128. 4093–4097. 15 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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