Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features
This addresses security concerns in identity verification and onboarding, but it is incremental as it builds on existing methods by adding handcrafted features.
The study tackled the problem of detecting deepfakes and face swaps in digital security by proposing a hybrid deep-learning framework that integrates handcrafted frequency-domain features with RGB inputs, resulting in enhanced detection capabilities against sophisticated manipulations.
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