Table 5.
The statistical table for GAN-generated image detection. GAN shows the GAN model used to generate the images. Method represents the detection algorithm. Performance is the obtained accuracy unless otherwise specified. Only the best performance described in each paper is reported in this table.
| GAN | Dataset | Method | Performance | |
|---|---|---|---|---|
| [82] | Cycle-GAN [87] | Cycle-GAN Data [87] | Cycle-GAN Discriminator [87] | 83.58% |
| Fridrich and Kodovsky [83] | 94.40% | |||
| Cozzolino et al. [84] | 95.07% | |||
| Bayar and Stamm [85] | 84.86% | |||
| Rahmouni et al. [58] | 85.71% | |||
| DenseNet [18] | 89.19% | |||
| InceptionNet V3 [86] | 89.09% | |||
| XceptionNet [19] | 94.49% | |||
| [88] | DC-GAN W-GAN |
CelebA [92] | DCGAN Discriminator | 95.51% |
| VGG+FLD | >90 % (DC-GAN) >94% (W-GAN) |
|||
| [91] | DFC-VAE DCGAN WGAN-GP PGGAN |
CelebAHQ [93] CelebA [92] LFW [94] |
Co-Color | 100% |
| [59] | PG-GAN | CelebAHQ [93] | Lap-CNN | 96.3% |
| [98] | GAN | MFS2018 [6] | RG-INHNet | 0.56 (AUC) |
| Saturation Features | 0.7 (AUC) | |||
| [100] | Cycle-GAN Pro-GAN Star-GAN |
MFS2018 [6] | PRNU-based method | 0.999 (AUC) |