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Evaluation Results#

Info

The content of this section is autogenerated based on latest published configuration of the TwinSpect Benchmark.

Overview#

Effectiveness of all algorithm/dataset pairs at optimum F1-Score:

Algorithm Dataset Threshold Recall Precision F1-Score
TEXT-CODE-64 NEWSEDITS-REUTERS-1000 15 0.97 0.98 0.98
IMAGE-CODE-64 MIRFLICKR-MFND 12 0.92 0.98 0.95
AUDIO-CODE-64 ISCC-FMA-10K 4 0.88 0.84 0.86
IMAGE-CODE-S-64 MIRFLICKR-MFND 13 0.91 0.98 0.94
IMAGE-CODE-SC-128 MIRFLICKR-MFND 36 0.97 0.98 0.97
TEXT-CODE-S-64 NEWSEDITS-REUTERS-1000 5 0.92 0.95 0.93
TEXT-CODE-SC-128 NEWSEDITS-REUTERS-1000 25 0.99 0.99 0.99

Results by Media Type#

Text#

Algorithm Dataset F1-Score
TEXT-CODE-64 NEWSEDITS-REUTERS-1000 0.98
TEXT-CODE-S-64 NEWSEDITS-REUTERS-1000 0.93
TEXT-CODE-SC-128 NEWSEDITS-REUTERS-1000 0.99

Image#

Algorithm Dataset F1-Score
IMAGE-CODE-64 MIRFLICKR-MFND 0.95
IMAGE-CODE-S-64 MIRFLICKR-MFND 0.94
IMAGE-CODE-SC-128 MIRFLICKR-MFND 0.97

Audio#

Algorithm Dataset F1-Score
AUDIO-CODE-64 ISCC-FMA-10K 0.86

Video#

No results available yet.