In one of the papers:
Effect of JPEG compression: In [36], the authors investigated
the sensitivity of their methods on compression. Using a compression method similar to Twitter, they trained their detection
methods on original uncompressed images and tested on JPEG
compressed images. The objective of this study was to test the
robustness of the detection techniques when images are posted
in social networks such as Twitter. In the second scenario, they
train and test on the JPEG compressed images. We performed
both of these experiments on the cycleGAN dataset. Since we are
not aware of the exact JPEG quantization tables used in Twitter,
our approach was similar but we tested on three different JPEG
quality factors (QF): 95, 85 and 75. We used 50% of the data for
training, 25% for validation and 25% for testing. The results are
reported on the 25% testing data of the cycleGAN dataset (9,076
images). As shown in Tab. 3, the accuracy progressively drops
as the QF decreases from 95-75, when trained on the original images. But when the JPEG compressed images are used for training, the accuracy shows a substantial increase. Even at a QF of
75, the accuracy is still 87.31%. This is also consistent with the
results reported in [36], where they report close to a 10% drop in
accuracy on Twitter-like compressed images.
In one of the papers: Effect of JPEG compression: In [36], the authors investigated the sensitivity of their methods on compression. Using a compression method similar to Twitter, they trained their detection methods on original uncompressed images and tested on JPEG compressed images. The objective of this study was to test the robustness of the detection techniques when images are posted in social networks such as Twitter. In the second scenario, they train and test on the JPEG compressed images. We performed both of these experiments on the cycleGAN dataset. Since we are not aware of the exact JPEG quantization tables used in Twitter, our approach was similar but we tested on three different JPEG quality factors (QF): 95, 85 and 75. We used 50% of the data for training, 25% for validation and 25% for testing. The results are reported on the 25% testing data of the cycleGAN dataset (9,076 images). As shown in Tab. 3, the accuracy progressively drops as the QF decreases from 95-75, when trained on the original images. But when the JPEG compressed images are used for training, the accuracy shows a substantial increase. Even at a QF of 75, the accuracy is still 87.31%. This is also consistent with the results reported in [36], where they report close to a 10% drop in accuracy on Twitter-like compressed images.