Table 3 Accuracies (%) for forgery... | Emerald Publishing

Table 3

Accuracies (%) for forgery detection using different approaches for the dataset MICC-F220.

Models	Accuracy $(%)$	TPR $(%)$	FPR $(%)$
Zernike [46]	–	20.91	6.36
GoDeep [9]	–	45.45	41.82
Zandi [22]	–	78.18	48.18
Li [47]	–	70.91	17.27
Cozzolino [48]	–	84.55	17.27
GMM	50.45	54.55	40.00
GGMM	53.64	64.09	36.36
BGGMM	55.45	58.33	45.94
BGGMM-Bhattacharyya kernel	57.65	58.99	40.96
BGGMM-kulback-leibler	60.19	63.76	52.20
BGGMM-fisher kernel	80.90	85.42	17.85

Models	Accuracy $(%)$	TPR $(%)$	FPR $(%)$
Zernike [46]	–	20.91	6.36
GoDeep [9]	–	45.45	41.82
Zandi [22]	–	78.18	48.18
Li [47]	–	70.91	17.27
Cozzolino [48]	–	84.55	17.27
GMM	50.45	54.55	40.00
GGMM	53.64	64.09	36.36
BGGMM	55.45	58.33	45.94
BGGMM-Bhattacharyya kernel	57.65	58.99	40.96
BGGMM-kulback-leibler	60.19	63.76	52.20
BGGMM-fisher kernel	80.90	85.42	17.85