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Digital media (e.g., photographs, video) can be easily created, edited, andshared. Tools for editing digital media are capable of doing so while alsomaintaining a high degree of photo-realism. While many types of edits todigital media are generally benign, others can also be applied for maliciouspurposes. State-of-the-art face editing tools and software can, for example,artificially make a person appear to be smiling at an inopportune time, ordepict authority figures as frail and tired in order to discredit individuals.Given the increasing ease of editing digital media and the potential risks frommisuse, a substantial amount of effort has gone into media forensics. To thisend, we created a challenge dataset of edited facial images to assist theresearch community in developing novel approaches to address and classify theauthenticity of digital media. Our dataset includes edits applied tocontrolled, portrait-style frontal face images and full-scene in-the-wildimages that may include multiple (i.e., more than one) face per image. Thegoals of our dataset is to address the following challenge questions: (1) Canwe determine the authenticity of a given image (edit detection)? (2) If animage has been edited, can we \textit{localize} the edit region? (3) If animage 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 datasetsspecifically curated to evaluate items (2) and (3), localization andclassification, respectively. Our hope is that our prepared evaluation protocolwill assist researchers in improving the state-of-the-art in image forensics asthey pertain to these challenges.

Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools