Jiangqun Ni

4.6k total citations · 2 hit papers
91 papers, 3.1k citations indexed

About

Jiangqun Ni is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Jiangqun Ni has authored 91 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Computer Vision and Pattern Recognition, 13 papers in Media Technology and 9 papers in Artificial Intelligence. Recurrent topics in Jiangqun Ni's work include Digital Media Forensic Detection (72 papers), Advanced Steganography and Watermarking Techniques (62 papers) and Chaos-based Image/Signal Encryption (40 papers). Jiangqun Ni is often cited by papers focused on Digital Media Forensic Detection (72 papers), Advanced Steganography and Watermarking Techniques (62 papers) and Chaos-based Image/Signal Encryption (40 papers). Jiangqun Ni collaborates with scholars based in China, United States and Taiwan. Jiangqun Ni's co-authors include Yuan Rao, Yang Yi, Jian Ye, Yun-Qing Shi, Linjie Guo, Yun Q. Shi, Wenkang Su, Jiwu Huang, Junxiang Wang and Chuntao Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Jiangqun Ni

86 papers receiving 3.0k citations

Hit Papers

Deep Learning Hierarchical Representations for Image Steg... 2016 2026 2019 2022 2017 2016 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Learning Hierarchical Representations for Image Steganalysis
    2017 487 citations Jiangqun Ni et al. IEEE Transactions on Information Forensics and Security profile →
  2. A deep learning approach to detection of splicing and copy-move forgeries in images
    2016 339 citations Yuan Rao, Jiangqun Ni profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiangqun Ni China 26 3.0k 386 346 145 142 91 3.1k
Xiangui Kang China 23 2.1k 0.7× 386 1.0× 352 1.0× 91 0.6× 391 2.8× 103 2.4k
Guangjie Liu China 18 899 0.3× 291 0.8× 406 1.2× 163 1.1× 192 1.4× 136 1.4k
Yuanman Li Macao 18 956 0.3× 148 0.4× 297 0.9× 59 0.4× 65 0.5× 54 1.2k
Zhiquan Wang China 17 1.1k 0.4× 121 0.3× 330 1.0× 57 0.4× 69 0.5× 47 1.3k
Benedetta Tondi Italy 18 749 0.3× 152 0.4× 498 1.4× 39 0.3× 179 1.3× 71 1.1k
Tomáš Pevný Czechia 22 2.7k 0.9× 182 0.5× 785 2.3× 34 0.2× 257 1.8× 59 3.1k
Min Long China 25 1.4k 0.5× 122 0.3× 254 0.7× 32 0.2× 389 2.7× 105 1.8k
Rongrong Ni China 28 2.8k 0.9× 385 1.0× 261 0.8× 87 0.6× 148 1.0× 121 2.9k
Rehan Ashraf Pakistan 19 666 0.2× 216 0.6× 187 0.5× 30 0.2× 65 0.5× 42 974
Jiwu Huang China 19 1.4k 0.5× 207 0.5× 92 0.3× 74 0.5× 165 1.2× 41 1.5k

Countries citing papers authored by Jiangqun Ni

Since Specialization
Citations

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

Fields of papers citing papers by Jiangqun Ni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiangqun Ni

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

All Works

20 of 20 papers shown
1.
Zhang, Yun, et al.. (2025). Leveraging High-Frequency Diversified Augmentation for general deepfake detection. Journal of Information Security and Applications. 89. 103994–103994. 1 indexed citations
2.
Ni, Jiangqun, et al.. (2024). DSM: Domain Shift Modeling for general deepfake detection. Signal Processing. 230. 109822–109822.
3.
Su, Wenkang, et al.. (2024). Efficient Audio Steganography Using Generalized Audio Intrinsic Energy With Micro-Amplitude Modification Suppression. IEEE Transactions on Information Forensics and Security. 19. 6559–6572. 3 indexed citations
4.
Ni, Jiangqun, et al.. (2024). FRADE: Forgery-aware Audio-distilled Multimodal Learning for Deepfake Detection. 6297–6306. 2 indexed citations
5.
Ni, Jiangqun, et al.. (2024). Domain-invariant and Patch-discriminative Feature Learning for General Deepfake Detection. ACM Transactions on Multimedia Computing Communications and Applications. 21(2). 1–19. 3 indexed citations
6.
Ni, Jiangqun, et al.. (2024). DIP: Diffusion Learning of Inconsistency Pattern for General DeepFake Detection. IEEE Transactions on Multimedia. 27. 2155–2167. 3 indexed citations
7.
Yang, Hongwei, Hui He, Weizhe Zhang, et al.. (2024). Backdoor Two-Stream Video Models on Federated Learning. ACM Transactions on Multimedia Computing Communications and Applications. 20(11). 1–20. 2 indexed citations
8.
Ni, Jiangqun, et al.. (2024). Efficient JPEG image steganography using pairwise conditional random field model. Signal Processing. 221. 109493–109493.
9.
Peng, Yi, et al.. (2024). Lite Localization Network and DUE-Based Watermarking for Color Image Copyright Protection. IEEE Transactions on Circuits and Systems for Video Technology. 34(10). 9311–9325. 8 indexed citations
10.
Su, Wenkang, Jiangqun Ni, & Yiyan Sun. (2024). StegaStyleGAN: Towards Generic and Practical Generative Image Steganography. Proceedings of the AAAI Conference on Artificial Intelligence. 38(1). 240–248. 9 indexed citations
11.
12.
Zhang, Ying, et al.. (2023). Reversible data hiding in enhanced images with anti-detection capability. Multimedia Tools and Applications. 83(4). 9853–9872. 2 indexed citations
13.
Zheng, Huicheng, et al.. (2023). Efficient Reversible Data Hiding Using Two-Dimensional Pixel Clustering. Electronics. 12(7). 1645–1645. 6 indexed citations
14.
Ni, Jiangqun, et al.. (2022). Evading generated-image detectors: A deep dithering approach. Signal Processing. 197. 108558–108558. 7 indexed citations
15.
Rao, Yuan, Jiangqun Ni, Weizhe Zhang, & Jiwu Huang. (2022). Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(5). 3285–3297. 10 indexed citations
16.
Ni, Jiangqun, et al.. (2021). Efficient JPEG Batch Steganography Using Intrinsic Energy of Image Contents. IEEE Transactions on Information Forensics and Security. 16. 4544–4558. 16 indexed citations
17.
Ni, Jiangqun, et al.. (2021). A Novel Video Steganographic Scheme Incorporating the Consistency Degree of Motion Vectors. IEEE Transactions on Circuits and Systems for Video Technology. 32(7). 4905–4910. 12 indexed citations
18.
Su, Wenkang, et al.. (2020). Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model. IEEE Transactions on Circuits and Systems for Video Technology. 31(3). 1001–1015. 58 indexed citations
19.
Su, Wenkang, et al.. (2018). A New Distortion Function Design for JPEG Steganography Using the Generalized Uniform Embedding Strategy. IEEE Transactions on Circuits and Systems for Video Technology. 28(12). 3545–3549. 57 indexed citations
20.
Hong, Wien, et al.. (2017). A Tunable Bound of the Embedding Level for Reversible Data Hiding with Contrast Enhancement. 網際網路技術學刊. 18(2). 409–415. 1 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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