Bayar-SrcOnly-s=qf(5)_t=qf(5).txt

    === FORGERY DETECTION TASK ===
    source : qf(5)
    target : qf(5)
    
    --- DATA ---
    source : random half images sampled from the "Splicing" category of the database DEFACTO
    repartition : 1-1/3 1/3
    
    A 3-fold cutting is applied to the source to split it into 3 train and test sets
    Then, the images in each cuts are transformed into batches of 128x128 patches.
    
    **In each set, there is a perfect balance between forged and non-forged patches : **
    
    -A patch associated to a forged region is kept in the sets only if the forged region occupy a space between 20% and 80% 
    of the total space (128x128). 
    -The real patches are chosen randomly so that there is an equal amount of forged and non-forged
    patches.
    -We kept only 2 patches for each class at maximum by image
    
    target : other half of the images from the "Splicing" category of the database DEFACTO and potentially presenting a different preprocessing compared to the source 
    (for instance a change in the quality factor for the compression).
    repartition : 1-1/3 1/3
    
    The preprocessing of the target images is the same as the one presented above
   
    --- TRAINING ---
    trainings_epochs on each fold : 30
    hyperparameters_file : hyperparameters-SrcOnly-s=qf(5)_t=qf(5).txt
    
    --- RESULTS --- 

    
qf(5) : 82.89999999999999% +/- 0.0%
qf(10) : 82.69999999999999% +/- 1.0%
qf(20) : 81.8% +/- 1.0%
qf(50) : 81.39999999999999% +/- 0.0%
qf(100) : 81.2% +/- 0.0%
none : 81.2% +/- 0.0%