leaderboard.csv

"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://www.resemble.ai/detect/"">Resemble.ai</a>","85.0685","94.73","75.41","81.15","90.4","82.24000000000001","90.83"
"2","<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"
"3","<a href=""https://arxiv.org/pdf/1901.08971"">Xception</a>","66.1301","99.32","32.95","63.82","67.36","69.01","66.67"
"4","<a href=""https://arxiv.org/abs/1912.11035"">ResNet101</a>","65.5137","97.67","33.36","63.13999999999999","67.19000000000001","66.67","67.55"
"5","<a href=""https://arxiv.org/pdf/1901.08971"">Xception SLADD</a>","65.0","99.86","30.14","62.470000000000006","65.62","67.17","67.36999999999999"
"6","<a href=""https://arxiv.org/abs/2109.01860"">STIL</a>","64.726","98.29","31.16","61.45","67.36","67.67","65.78"
"7","<a href=""https://arxiv.org/abs/1912.11035"">ResNet34</a>","63.9726","98.36","29.59","61.79","65.79","64.99000000000001","65.61"
"8","<a href=""https://www.iieta.org/download/file/fid/96533"">VGG19</a>","60.6849","99.45","21.92","57.48","61.78","63.99","62.79"
"9","<a href=""https://arxiv.org/abs/1905.11946"">EfficientNetB4</a>","58.2192","99.66000000000001","16.78","55.54","60.56","61.14000000000001","58.379999999999995"
"10","<a href=""https://arxiv.org/abs/2103.00020"">CLIP</a>","55.4452","94.04","16.85","54.61","57.42","56.620000000000005","53.97"
"11","<a href=""https://arxiv.org/pdf/1901.08971"">Xception FFD</a>","54.8288","97.33000000000001","12.33","53.68000000000001","56.37","56.279999999999994","54.14"