Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features

TL;DR

This paper introduces a hybrid deepfake detection method that combines deep learning with handcrafted frequency features to improve detection accuracy against sophisticated face manipulations.

Contribution

It proposes a novel hybrid framework integrating frequency-domain handcrafted features with deep learning for enhanced deepfake detection.

Findings

Improved detection accuracy over baseline models

Effective identification of manipulation artifacts in frequency domain

Robustness against various deepfake techniques

Abstract

The rapid advancement of deepfake and face swap technologies has raised significant concerns in digital security, particularly in identity verification and onboarding processes. Conventional detection methods often struggle to generalize against sophisticated facial manipulations. This study proposes an enhanced deep-learning detection framework that combines handcrafted frequency-domain features with conventional RGB inputs. This hybrid approach exploits frequency and spatial domain artifacts introduced during image manipulation, providing richer and more discriminative information to the classifier. Several frequency handcrafted features were evaluated, including the Steganalysis Rich Model, Discrete Cosine Transform, Error Level Analysis, Singular Value Decomposition, and Discrete Fourier Transform