Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools
Brian DeCann, Kirill Trapeznikov

TL;DR
This paper introduces a comprehensive dataset of face-manipulated images to aid in developing and evaluating forensic tools for detecting, localizing, and classifying digital media edits, addressing a critical need in media forensics.
Contribution
The paper presents a new challenge dataset of facial image edits, including localization and classification tasks, filling a gap in existing datasets for forensic research.
Findings
Dataset enables evaluation of edit detection, localization, and classification.
Supports development of forensic tools for real-world media manipulation.
Facilitates benchmarking and comparison of forensic methods.
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis · Digital Media Forensic Detection · Law in Society and Culture
