Yinpeng Dong

8.4k total citations · 1 hit paper
40 papers, 1.5k citations indexed

About

Yinpeng Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Yinpeng Dong has authored 40 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 21 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Yinpeng Dong's work include Adversarial Robustness in Machine Learning (27 papers), Anomaly Detection Techniques and Applications (11 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Yinpeng Dong is often cited by papers focused on Adversarial Robustness in Machine Learning (27 papers), Anomaly Detection Techniques and Applications (11 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Yinpeng Dong collaborates with scholars based in China, Singapore and United States. Yinpeng Dong's co-authors include Jun Zhu, Hang Su, Tianyu Pang, Baoyuan Wu, Wei Liu, Zhifeng Li, Tong Zhang, Hang Su, Zihao Xiao and Xiao Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Information Sciences.

In The Last Decade

Yinpeng Dong

35 papers receiving 1.4k citations

Hit Papers

Evading Defenses to Transferable Adversarial Examples by ... 2019 2026 2021 2023 2019 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. Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
    2019 488 citations Yinpeng Dong, Tianyu Pang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yinpeng Dong China 17 1.2k 688 292 133 97 40 1.5k
Cihang Xie United States 14 1.4k 1.2× 850 1.2× 234 0.8× 178 1.3× 109 1.1× 33 1.8k
Ivan Evtimov United States 4 853 0.7× 205 0.3× 263 0.9× 123 0.9× 104 1.1× 5 1.0k
Kevin Eykholt United States 6 908 0.8× 215 0.3× 316 1.1× 126 0.9× 106 1.1× 12 1.1k
Aishan Liu China 16 626 0.5× 387 0.6× 109 0.4× 55 0.4× 66 0.7× 55 867
Yuan He China 19 639 0.6× 1.0k 1.5× 141 0.5× 48 0.4× 44 0.5× 58 1.5k
Brody Huval United States 3 986 0.9× 516 0.8× 40 0.1× 69 0.5× 67 0.7× 3 1.5k
Benedikt Gierlichs Belgium 17 726 0.6× 294 0.4× 251 0.9× 27 0.2× 496 5.1× 45 949
Xiaohui Kuang China 18 536 0.5× 137 0.2× 206 0.7× 47 0.4× 33 0.3× 80 897
Wassim Hamidouche France 18 181 0.2× 727 1.1× 512 1.8× 32 0.2× 42 0.4× 116 1.1k

Countries citing papers authored by Yinpeng Dong

Since Specialization
Citations

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

Fields of papers citing papers by Yinpeng Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yinpeng Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Yinpeng Dong. A scholar is included among the top collaborators of Yinpeng Dong 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 Yinpeng Dong. Yinpeng Dong 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.
Dong, Yinpeng, et al.. (2025). Align Is Not Enough: Multimodal Universal Jailbreak Attack Against Multimodal Large Language Models. IEEE Transactions on Circuits and Systems for Video Technology. 35(6). 5475–5488.
2.
Wei, Xingxing, et al.. (2025). Distributionally Location-Aware Transferable Adversarial Patches for Facial Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(4). 2849–2864. 1 indexed citations
3.
Dong, Yinpeng, et al.. (2025). Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-Training Models With Contrastive Learning. IEEE Transactions on Multimedia. 27. 6410–6421. 1 indexed citations
4.
Dong, Yinpeng, et al.. (2025). Real-World Adversarial Defense Against Patch Attacks Based on Diffusion Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(12). 11124–11140.
5.
Dong, Yinpeng, Xingxing Zhang, Liyuan Wang, et al.. (2025). RobustPrompt: Learning to defend against adversarial attacks with adaptive visual prompts. Pattern Recognition Letters. 190. 161–168.
6.
Zhang, Jinlai, Yinpeng Dong, Jun Zhu, et al.. (2024). Improving transferability of 3D adversarial attacks with scale and shear transformations. Information Sciences. 662. 120245–120245. 14 indexed citations
7.
Dong, Yinpeng, et al.. (2024). Diffusion Models are Certifiably Robust Classifiers. 50062–50097. 2 indexed citations
8.
Dong, Yinpeng, et al.. (2023). Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning. 1577–1587. 17 indexed citations
9.
Dong, Yinpeng, et al.. (2023). Competition on robust deep learning. National Science Review. 10(6). nwad087–nwad087. 1 indexed citations
10.
Dong, Yinpeng, et al.. (2023). Batch virtual adversarial training for graph convolutional networks. SHILAP Revista de lepidopterología. 4. 73–79. 7 indexed citations
11.
Yang, Xiao, Chang Liu, Yikai Wang, et al.. (2023). Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition. 4119–4128. 18 indexed citations
12.
Xiao, Zihao, Yinpeng Dong, Wei Gao, et al.. (2021). Improving Transferability of Adversarial Patches on Face Recognition with Generative Models. 11840–11849. 66 indexed citations
13.
Pang, Tianyu, Kun Xu, Yinpeng Dong, et al.. (2020). Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness. arXiv (Cornell University). 5 indexed citations
14.
Pang, Tianyu, Xiao Yang, Yinpeng Dong, et al.. (2020). Boosting Adversarial Training with Hypersphere Embedding. Neural Information Processing Systems. 33. 7779–7792. 12 indexed citations
15.
Dong, Yinpeng, Qian Fu, Xiao Yang, et al.. (2020). Benchmarking Adversarial Robustness on Image Classification. 318–328. 133 indexed citations
16.
Dong, Yinpeng, et al.. (2019). Improving Black-box Adversarial Attacks with a Transfer-based Prior. arXiv (Cornell University). 32. 10932–10942. 24 indexed citations
17.
Dong, Yinpeng, Tianyu Pang, Hang Su, & Jun Zhu. (2018). Evading Defenses to Transferable Adversarial Examples by Mitigating Attention Shift. 1 indexed citations
18.
Dong, Yinpeng, Fangzhou Liao, Tianyu Pang, Xiaolin Hu, & Jun Zhu. (2017). Discovering Adversarial Examples with Momentum. arXiv (Cornell University). 30 indexed citations
19.
Su, Hang, Jun Zhu, Yinpeng Dong, & Bo Zhang. (2017). Forecast the Plausible Paths in Crowd Scenes. 2772–2778. 49 indexed citations
20.
Su, Hang, Yinpeng Dong, Jun Zhu, Haibin Ling, & Bo Zhang. (2016). Crowd scene understanding with coherent recurrent neural networks. International Joint Conference on Artificial Intelligence. 3469–3476. 28 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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