# FakeParts: a New Family of AI-Generated DeepFakes

## TL;DR

FakeParts introduces a new class of deepfakes involving subtle, localized manipulations, and provides a large-scale benchmark to evaluate detection methods, revealing significant vulnerabilities in current detectors.

## Contribution

The paper presents FakeParts, a novel type of partial deepfake, and FakePartsBench, the first large-scale dataset for evaluating detection of such manipulations.

## Key findings

- FakeParts reduces human detection accuracy by up to 26%.
- State-of-the-art detectors also perform poorly on FakeParts.
- FakeParts exposes vulnerabilities in current deepfake detection methods.

## Abstract

We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations - ranging from altered facial expressions to object substitutions and background modifications - blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address the critical gap in detection, we present FakePartsBench, the first large-scale benchmark specifically designed to capture the full spectrum of partial deepfakes. Comprising over 81K (including 44K FakeParts) videos with pixel- and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by up to 26% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current detectors and provides the necessary resources to develop methods robust to partial manipulations.