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