Welcome! Computer vision algorithms often work well on some images, but fail on others. Ours is like this too. We believe our work is a significant step forward in detecting and undoing facial warping by image editing tools. However, there are still many hard cases, and this is by no means a solved problem.
This is partly because our algorithm is trained on faces warped by the Face-aware Liquify tool in Photoshop, and will thus work well for these types of images, but not necessarily for others. We call this the "dataset bias" problem. Please see the paper for more details on this issue.
While we trained our models with various data augmentation to be more robust to downstream operations such as resizing, jpeg compression and saturation/brightness changes, there are many other retouches (e.g. airbrushing) that can alter the low-level statistics of the images to make the detection a really hard one.
Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to "undo" a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations.
We thank Daichi Ito and Adam Pintek for contributing to our artist testset, and
Jacob Huh for the helpful discussions. This work was supported, in part, by DARPA MediFor
and UC Berkeley Center for Long-Term Cybersecurity. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
