Skip to main content

Table 5 Realistic performance of the individual detectors and fusion approaches with the fixed decision thresholds and equal fusion weights with the combined LondonDB/UtrechtDB/Alabama datasets (best result per morph type 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

   SC1 SC2
Detector Morph type BPCER APCER HTER BPCER APCER HTER
DArXivMC Combined 7.45% 1.00% 4.22% 3.87% 18.56% 11.22%
DArXivNaive   2.01% 1.82% 1.91% 0.97% 13.32% 7.14%
DBIOSIGMC   25.76% 1.35% 13.56% 23.10% 10.12% 16.61%
DBIOSIGNaive   11.47% 5.25% 8.36% 9.54% 18.05% 13.80%
Dkeypoints   87.86% 0.00% 43.93% 96.94% 0.00% 48.47%
FM   11.39% 0.56% 5.97% 7.15% 9.30% 8.23%
FWLC   18.09% 0.02% 9.05% 19.90% 0.84% 10.37%
FDST   25.47% 0.02% 12.74% 35.02% 0.01% 17.52%
FLR   23.68% 0.02% 11.85% 27.05% 0.35% 13.70%
DArXivMC Complete 7.45% 1.00% 4.22% 3.87% 15.73% 9.80%
DArXivNaive   2.01% 1.60% 1.81% 0.97% 10.57% 5.77%
DBIOSIGMC   25.76% 1.38% 13.57% 23.10% 8.38% 15.74%
DBIOSIGNaive   11.47% 4.47% 7.97% 9.54% 14.31% 11.92%
Dkeypoints   87.86% 0.00% 43.93% 96.94% 0.00% 48.47%
FM   11.39% 0.31% 5.85% 7.15% 3.76% 5.46%
FWLC   18.09% 0.02% 9.05% 19.90% 0.60% 10.25%
FDST   25.47% 0.02% 12.74% 35.02% 0.02% 17.52%
FLR   23.68% 0.02% 11.85% 27.05% 0.27% 13.66%
DArXivMC Splicing 7.45% 2.57% 5.01% 3.87% 24.81% 14.34%
DArXivNaive   2.01% 3.54% 2.77% 0.97% 16.04% 8.50%
DBIOSIGMC   25.76% 3.54% 14.65% 23.10% 17.39% 20.25%
DBIOSIGNaive   11.47% 7.39% 9.43% 9.54% 21.22% 15.38%
Dkeypoints   87.86% 0.02% 43.94% 96.94% 0.00% 48.47%
FM   11.39% 1.45% 6.42% 7.15% 12.35% 9.75%
FWLC   18.09% 0.07% 9.08% 19.90% 1.42% 10.66%
FDST   25.47% 0.03% 12.75% 35.02% 0.03% 17.52%
FLR   23.68% 0.03% 11.85% 27.05% 0.55% 13.80%