Robust Deepfake Detection, NTIRE 2026 Challenge: Report
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arXiv
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| Natura: | Preprint |
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2026
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| author | Hopf, Benedikt Timofte, Radu Qu, Chenfan Li, Junchi Wu, Fei Lu, Dagong Yao, Mufeng Xu, Xinlei Guo, Fengjun Tang, Yongwei Yang, Zhiqiang Wu, Zhiqiang Seow, Jia Wen Koay, Hong Vin Ren, Haodong Xu, Feng Chen, Shuai Le-Phan, Minh-Khoa Le, Minh-Hoang Do, Trong-Le Tran, Minh-Triet Jian, Chih-Yu Wang, Yi-Fan Chen, Bang-Kang Chao, You-Chen Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung Negi, Aashish Sharma, Hardik Shaily, Prateek Kumar, Jayant Chaudhary, Sachin Dudhane, Akshay Hambarde, Praful Shukla, Amit Peng, Jielun Wang, Yabin Li, Yaqi Liu, Jincheng Hong, Xiaopeng Wadhwani, Krish Fitzpatrick, Liam Tiwari, Utkarsh Benjdira, Bilel Ali, Anas M. Boulila, Wadii Quispe, Cristian Lazo A, Aishwarya S, Akshara N, Ashwathi Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| author_facet | Hopf, Benedikt Timofte, Radu Qu, Chenfan Li, Junchi Wu, Fei Lu, Dagong Yao, Mufeng Xu, Xinlei Guo, Fengjun Tang, Yongwei Yang, Zhiqiang Wu, Zhiqiang Seow, Jia Wen Koay, Hong Vin Ren, Haodong Xu, Feng Chen, Shuai Le-Phan, Minh-Khoa Le, Minh-Hoang Do, Trong-Le Tran, Minh-Triet Jian, Chih-Yu Wang, Yi-Fan Chen, Bang-Kang Chao, You-Chen Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung Negi, Aashish Sharma, Hardik Shaily, Prateek Kumar, Jayant Chaudhary, Sachin Dudhane, Akshay Hambarde, Praful Shukla, Amit Peng, Jielun Wang, Yabin Li, Yaqi Liu, Jincheng Hong, Xiaopeng Wadhwani, Krish Fitzpatrick, Liam Tiwari, Utkarsh Benjdira, Bilel Ali, Anas M. Boulila, Wadii Quispe, Cristian Lazo A, Aishwarya S, Akshara N, Ashwathi Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Deepfake Detection, NTIRE 2026 Challenge: Report Hopf, Benedikt Timofte, Radu Qu, Chenfan Li, Junchi Wu, Fei Lu, Dagong Yao, Mufeng Xu, Xinlei Guo, Fengjun Tang, Yongwei Yang, Zhiqiang Wu, Zhiqiang Seow, Jia Wen Koay, Hong Vin Ren, Haodong Xu, Feng Chen, Shuai Le-Phan, Minh-Khoa Le, Minh-Hoang Do, Trong-Le Tran, Minh-Triet Jian, Chih-Yu Wang, Yi-Fan Chen, Bang-Kang Chao, You-Chen Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung Negi, Aashish Sharma, Hardik Shaily, Prateek Kumar, Jayant Chaudhary, Sachin Dudhane, Akshay Hambarde, Praful Shukla, Amit Peng, Jielun Wang, Yabin Li, Yaqi Liu, Jincheng Hong, Xiaopeng Wadhwani, Krish Fitzpatrick, Liam Tiwari, Utkarsh Benjdira, Bilel Ali, Anas M. Boulila, Wadii Quispe, Cristian Lazo A, Aishwarya S, Akshara N, Ashwathi Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Computer Vision and Pattern Recognition 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. |
| title | Robust Deepfake Detection, NTIRE 2026 Challenge: Report |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.24163 |