  Digital media (e.g., photographs, video) can be easily created, edited, and
shared. Tools for editing digital media are capable of doing so while also
maintaining a high degree of photo-realism. While many types of edits to
digital media are generally benign, others can also be applied for malicious
purposes. State-of-the-art face editing tools and software can, for example,
artificially make a person appear to be smiling at an inopportune time, or
depict authority figures as frail and tired in order to discredit individuals.
Given the increasing ease of editing digital media and the potential risks from
misuse, a substantial amount of effort has gone into media forensics. To this
end, we created a challenge dataset of edited facial images to assist the
research community in developing novel approaches to address and classify the
authenticity of digital media. Our dataset includes edits applied to
controlled, portrait-style frontal face images and full-scene in-the-wild
images that may include multiple (i.e., more than one) face per image. The
goals of our dataset is to address the following challenge questions: (1) Can
we determine the authenticity of a given image (edit detection)? (2) If an
image has been edited, can we \textit{localize} the edit region? (3) If an
image has been edited, can we deduce (classify) what edit type was performed?
The majority of research in image forensics generally attempts to answer item
(1), detection. To the best of our knowledge, there are no formal datasets
specifically curated to evaluate items (2) and (3), localization and
classification, respectively. Our hope is that our prepared evaluation protocol
will assist researchers in improving the state-of-the-art in image forensics as
they pertain to these challenges.
