# Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks

Source: [Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks](<https://www.sota2.com/research/paper/68fa6d3b1e209131cea38a16>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:54.812Z

Authors: Christeen T Jose

Published: 2022-08-15T03:32:28.000Z

## Abstract

This paper presents our results and findings on the use of temporal images for deepfake detection. We modelled temporal relations that exist in the movement of 468 facial landmarks across frames of a given video as spatial relations by constructing an image (referred to as temporal image) using the pixel values at these facial landmarks. CNNs are capable of recognizing spatial relationships that exist between the pixels of a given image. 10 different ImageNet models were considered for the study.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2208.06990>)

[PDF](<https://arxiv.org/pdf/2208.06990.pdf>)