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Table 9 Fusion under laboratory conditions: tenfold stratified cross-validation with 90% training/10% test split; genuine samples from the Alabama dataset [53]; morphs from LondonDB and UtrechtDB (best result per morph type and application scenario marked in bold)

From: Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks

 

Combined

Complete

Splicing

SC1

SC2

SC1

SC2

SC1

SC2

EER

HTER

EER

HTER

EER

HTER

EER

HTER

EER

HTER

EER

HTER

Fixed

DArXivMC

3.8%

4.3%

8.2%

11.2%

3.9%

4.2%

7.2%

9.8%

4.8%

5.0%

9.2%

14.3%

DArXivNaive

1.5%

1.9%

3.9%

7.1%

1.3%

1.8%

3.4%

5.8%

1.8%

2.7%

4.4%

8.5%

DBIOSIGMC

9.3%

13.6%

15.7%

16.6%

9.3%

13.6%

14.7%

15.7%

12.8%

14.7%

20.2%

20.2%

DBIOSIGNaive

7.1%

8.4%

13.4%

13.7%

7.0%

7.9%

11.8%

11.9%

8.4%

9.4%

14.2%

15.4%

Dkeypoints

12.3%

43.9%

18.6%

48.8%

12.2%

43.9%

19.3%

48.8%

8.9%

43.9%

18.3%

48.8%

FM

 

6.0%

 

8.2%

 

6.2%

 

5.9%

 

6.4%

 

9.7%

FWLC

 

9.6%

 

10.9%

 

9.2%

 

10.6%

 

9.2%

 

10.6%

FDST

 

2.6%

 

5.9%

 

3.0%

 

6.7%

 

2.9%

 

7.3%

FLR

 

2.6%

 

5.9%

 

3.0%

 

6.7%

 

2.9%

 

7.3%

Adaptive

DArXivMC

3.8%

3.9%

8.2%

8.3%

3.9%

4.0%

7.2%

7.9%

4.8%

4.9%

9.2%

9.4%

DArXivNaive

1.5%

2.2%

3.9%

5.0%

1.3%

2.1%

3.4%

4.4%

1.8%

3.0%

4.4%

5.6%

DBIOSIGMC

9.3%

9.8%

15.7%

16.4%

9.3%

9.8%

14.7%

15.0%

12.8%

12.7%

20.2%

20.7%

DBIOSIGNaive

7.1%

7.9%

13.4%

13.6%

7.0%

7.6%

11.8%

12.1%

8.4%

9.1%

14.2%

15.0%

Dkeypoints

12.3%

12.7%

18.6%

19.0%

12.2%

12.5%

19.3%

19.3%

8.9%

11.4%

18.3%

19.2%

FM

 

2.8%

 

6.0%

 

2.2%

 

4.5%

 

3.3%

 

6.8%

FWLC

 

15.2%

 

39.2%

 

14.3%

 

35.5%

 

17.7%

 

45.8%

FDST

 

2.8%

 

5.8%

 

3.3%

 

6.6%

 

3.1%

 

7.3%

FLR

 

2.8%

 

5.8%

 

3.3%

 

6.6%

 

3.1%

 

7.3%