Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis

TL;DR

VideoSham is a diverse dataset of real and manipulated videos designed to evaluate and improve AI detection algorithms and human ability to identify manipulated media beyond facial edits.

Contribution

The paper introduces VideoSham, a novel dataset with diverse, high-resolution, human-centric manipulations, and analyzes the limitations of current detection algorithms and human perception.

Findings

State-of-the-art algorithms perform poorly on diverse manipulations.

Humans can sometimes detect manipulations better than algorithms.

Current AI methods do not generalize well to complex, real-world manipulated videos.

Abstract

As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a greater need to distinguish between ``real'' and ``manipulated'' content. To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated). Many of the existing deepfake datasets focus exclusively on two types of facial manipulations -- swapping with a different subject's face or altering the existing face. VideoSham, on the other hand, contains more diverse, context-rich, and human-centric, high-resolution videos manipulated using a combination of 6 different spatial and temporal attacks. Our analysis shows that state-of-the-art manipulation detection algorithms only work for a few specific attacks and do not scale well on VideoSham. We performed a user study on Amazon Mechanical Turk with 1200 participants to understand if they can differentiate between the real and manipulated videos in VideoSham. Finally, we dig deeper into the strengths and weaknesses of performances by humans and SOTA-algorithms to identify gaps that need to be filled with better AI algorithms. We present the dataset at https://github.com/adobe-research/VideoSham-dataset.