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leaderboard.csv

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-Rank,Model,Accuracy,Accuracy on Real,Accuracy on Fake,Accuracy on JPEG,Accuracy on PNG,Accuracy on WEBP,Accuracy on TIFF
-1,<a href="test">RECCE</a>,67.3,99.4,35.1,64.2,69.5,68.8,69.8
-2,Xception,66.1,99.3,33.0,63.8,67.4,69.0,66.7
-3,ResNet101,65.5,97.7,33.4,63.1,67.2,66.7,67.6
-4,Xception SLADD,65.0,99.9,30.1,62.5,65.6,67.2,67.4
-5,STIL,64.7,98.3,31.2,61.4,67.4,67.7,65.8
-6,ResNet34,64.0,98.4,29.6,61.8,65.8,65.0,65.6
-7,VGG19,60.7,99.4,21.9,57.5,61.8,64.0,62.8
-8,EfficientNetB4,58.2,99.7,16.8,55.5,60.6,61.1,58.4
-9,CLIP,55.4,94.0,16.8,54.6,57.4,56.6,54.0
-10,Xception FFD,54.8,97.3,12.3,53.7,56.4,56.3,54.1
+"Rank","Model","Accuracy","Accuracy on Real","Accuracy on Fake","Accuracy on JPEG","Accuracy on PNG","Accuracy on WEBP","Accuracy on TIFF"
+"1","<a href=""https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_End-to-End_Reconstruction-Classification_Learning_for_Face_Forgery_Detection_CVPR_2022_paper.pdf"">RECCE</a>","67.2603","99.38","35.14","64.16","69.46","68.84","69.84"
+"2","Xception","66.1301","99.32","32.95","63.82","67.36","69.01","66.67"
+"3","ResNet101","65.5137","97.67","33.36","63.13999999999999","67.19000000000001","66.67","67.55"
+"4","Xception SLADD","65.0","99.86","30.14","62.470000000000006","65.62","67.17","67.36999999999999"
+"5","<a href=""https://arxiv.org/abs/2109.01860"">STIL</a>","64.726","98.29","31.16","61.45","67.36","67.67","65.78"
+"6","ResNet34","63.9726","98.36","29.59","61.79","65.79","64.99000000000001","65.61"
+"7","VGG19","60.6849","99.45","21.92","57.48","61.78","63.99","62.79"
+"8","EfficientNetB4","58.2192","99.66000000000001","16.78","55.54","60.56","61.14000000000001","58.379999999999995"
+"9","<a href=""https://arxiv.org/abs/2103.00020"">CLIP</a>","55.4452","94.04","16.85","54.61","57.42","56.620000000000005","53.97"
+"10","Xception FFD","54.8288","97.33000000000001","12.33","53.68000000000001","56.37","56.279999999999994","54.14"