Table 4.

The experimental setting for different algorithms for recaptured image forensics (RF), CG image detection (CGI), and Source social networks identification (SSN). In this table, DA, FE, PS, C denote respectively data augmentation, fusion and ensemble, patch selection, and classifiers. The ratio between training and test data is shown in the column “Train: Test”. For the performance-patch/voting, the numbers between parenthesis denote the patch sizes, when applicable.

		Arch.	Input Size	D.A.	F./E.	P.S.	C.	Train: Test	Dataset	Perf. (Patch)	Perf. (Voting)
RF	[50]	B1	N × N × 3	✓	-	-	Softmax	1:1	NTU-Rose [65] LCD_R [66]	99.74% (512) 99.30% (256) 98.48% (128) 95.23% (64)
	[53]	B2	64 × 64 × 1	✓	✓	-	Softmax	8:2	LS-D [53]	99.90%
	[52]	B3	32 × 32 × 3	✓	-	-	Softmax	1:1	ASTAR [67]	86.78%	93.29% (64)
									NTU-Rose [65]	96.93%	98.67% (64)
									ICL [68]	97.79%	99.54% (64)
	[51]	B4	64 × 64 × 3	-	-	-	Softmax	1:1	ICL [68]	85.73%	96.60%
CGI	[54]	C1	32 × 32 × 3	-	-	-	Softmax	3:1	Columbia [69]		98%
	[70]	ResNet50	224 × 224	-	-	-	Softmax	5-f CV	DSTok [71]	96.1%
	[55]	C2	96 × 96	✓	✓	-	Softmax	13:4	3Dlink [55]	90.79%	94.87% (192)
	[56]	C3	650 × 650	✓	-	-	Softmax	9:8	WIFS [58]	99.95%	100%
	[72]	ResNet50	?	✓	-	-	Softmax	7:1	Columbia [69]	98%
	[57]	C4	233 × 233	✓	-	✓	Softmax	3:1	Columbia [69]	85.15%	93.20%
	[58]	C5	100 × 100 × 1	-	✓	-	MLP	8:2	WIFS [58]	84.80%	93.20%
	[73]	VGG19		-	✓	✓	MLP	5:2	WIFS [58]	96.55%	99.89%
	[74]	ResNet50	224 × 224 × 3	-	-	-	SVM		DSTok [71]	94%
SSN	[60]	E1	64 × 64	✓	-	-	Softmax	9:1	UCID [75]	98.41%	95% (Avg.)
									PUBLIC [75]	87.60%
									IPLAB [76]	90.89%
	[61]	E2	64 × 64	✓	-	-	Softmax	9:1	UCID [75]	79.49%	90.83%
									VISION [64]	98.50%
									IPLAB [76]		83.85%