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arXiv · 2604.24163

Robust Deepfake Detection, NTIRE 2026 Challenge: Report

Benedikt Hopf·Radu Timofte·Chenfan Qu·Junchi Li·Fei Wu·Dagong Lu·Mufeng Yao·Xinlei Xu·Fengjun Guo·Yongwei Tang·Zhiqiang Yang·Zhiqiang Wu·Jia Wen Seow·Hong Vin Koay·Haodong Ren·Feng Xu·Shuai Chen·Minh-Khoa Le-Phan·Minh-Hoang Le·Trong-Le Do·Minh-Triet Tran·Chih-Yu Jian·Yi-Fan Wang·Bang-Kang Chen·You-Chen Chao·Chia-Ming Lee·Fu-En Yang·Yu-Chiang Frank Wang·Chih-Chung Hsu·Aashish Negi·Hardik Sharma·Prateek Shaily·Jayant Kumar·Sachin Chaudhary·Akshay Dudhane·Praful Hambarde·Amit Shukla·Jielun Peng·Yabin Wang·Yaqi Li·Jincheng Liu·Xiaopeng Hong·Krish Wadhwani·Liam Fitzpatrick·Utkarsh Tiwari·Bilel Benjdira·Anas M. Ali·Wadii Boulila·Cristian Lazo Quispe·Aishwarya A·Akshara S·Ashwathi N·Jiachen Tu·Guoyi Xu·Yaoxin Jiang·Jiajia Liu·Yaokun Shi
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Abstract

Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight image degradation. In addition to weaker degradations that can accidentally occur in the image processing pipeline, there is another risk of malicious deepfakes that specifically introduce degradations, purposefully exploiting the detector's weaknesses in that regard. Here, we present an overview of the NTIRE 2026 Robust Deepfake Detection Challenge, which specifically addresses that problem. Participants were tasked with building a detector that would later be tested on an unknown test-set, which included both common and uncommon degradations of various strengths. With a total number of 337 participants and 57 submissions to the final leaderboard, the first edition of the challenge was well received. To ensure the reliability of the results, participants were given only 24h to complete the test run with no labels provided, limiting the possibility of training on the test data. Furthermore, the top solutions were scored on a private test-set to detect any such overfitting. This report presents the competition setting, dataset preparation, as well as details and performance of methods. Top methods rely on large foundation models, ensembles, and degradation training to combine generality and robustness.

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Benedikt Hopf, Radu Timofte, Chenfan Qu, Junchi Li, Fei Wu, Dagong Lu, Mufeng Yao, Xinlei Xu, Fengjun Guo, Yongwei Tang, Zhiqiang Yang, Zhiqiang Wu, Jia Wen Seow, Hong Vin Koay, Haodong Ren, Feng Xu, Shuai Chen, Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do, Minh-Triet Tran, Chih-Yu Jian, Yi-Fan Wang, Bang-Kang Chen, You-Chen Chao, Chia-Ming Lee, Fu-En Yang, Yu-Chiang Frank Wang, Chih-Chung Hsu, Aashish Negi, Hardik Sharma, Prateek Shaily, Jayant Kumar, Sachin Chaudhary, Akshay Dudhane, Praful Hambarde, Amit Shukla, Jielun Peng, Yabin Wang, Yaqi Li, Jincheng Liu, Xiaopeng Hong, Krish Wadhwani, Liam Fitzpatrick, Utkarsh Tiwari, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Cristian Lazo Quispe, Aishwarya A, Akshara S, Ashwathi N, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi. 2026-04-27. Robust Deepfake Detection, NTIRE 2026 Challenge: Report. https://arxiv.org/abs/2604.24163

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