Research Article
Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues
Table 2
Quantitative comparison results in terms of Acc and AUC on FF++ dataset with four manipulation methods.
| Methods | Manipulations (LQ) | Deepfake | Face2Face | FaceSwap | NeuralTexture | Metrics | Acc | AUC | Acc | AUC | Acc | AUC | Acc | AUC |
| Xception [20] | 92.81 | 94.32 | 85.21 | 87.04 | 91.84 | 93.83 | 75.21 | 77.67 | EfficientNet [39] | 93.12 | 95.64 | 85.32 | 87.21 | 92.38 | 94.23 | 76.41 | 79.13 | Vit [15] | 79.86 | 82.78 | 67.91 | 69.34 | 76.65 | 79.18 | 65.43 | 68.78 | Swin-B [32] | 85.25 | 88.32 | 76.68 | 78.12 | 83.43 | 85.16 | 72.31 | 75.14 | GFFD [9] | 94.02 | 96.01 | 86.02 | 88.23 | 92.52 | 94.22 | 79.21 | 82.97 | MADD [7] | 94.47 | 96.43 | 86.87 | 89.42 | 93.66 | 95.45 | 81.21 | 83.61 | Ours | 95.58 | 97.01 | 88.23 | 90.81 | 94.74 | 96.16 | 83.46 | 86.02 |
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The best results are marked in bold fonts.
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