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

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 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.