SFE-Net: Harnessing Biological Principles of Differential Gene Expression for Improved Feature Selection in Deep Learning Networks

TL;DR

SFE-Net introduces a biologically inspired dynamic feature selection mechanism to improve deepfake detection, enhancing adaptability and robustness across diverse datasets.

Contribution

The paper presents SFE-Net, a novel deep learning framework that dynamically adjusts feature importance inspired by differential gene expression, improving deepfake detection accuracy and generalization.

Findings

Outperforms existing static models in deepfake detection accuracy.

Shows improved generalization across different deepfake datasets.

Reduces overfitting by balancing feature exploration and exploitation.

Abstract

In the realm of DeepFake detection, the challenge of adapting to various synthesis methodologies such as Faceswap, Deepfakes, Face2Face, and NeuralTextures significantly impacts the performance of traditional machine learning models. These models often suffer from static feature representation, which struggles to perform consistently across diversely generated deepfake datasets. Inspired by the biological concept of differential gene expression, where gene activation is dynamically regulated in response to environmental stimuli, we introduce the Selective Feature Expression Network (SFE-Net). This innovative framework integrates selective feature activation principles into deep learning architectures, allowing the model to dynamically adjust feature priorities in response to varying deepfake generation techniques. SFE-Net employs a novel mechanism that selectively enhances critical features essential for accurately detecting forgeries, while reducing the impact of irrelevant or misleading cues akin to adaptive evolutionary processes in nature. Through rigorous testing on a range of deepfake datasets, SFE-Net not only surpasses existing static models in detecting sophisticated forgeries but also shows enhanced generalization capabilities in cross-dataset scenarios. Our approach significantly mitigates overfitting by maintaining a dynamic balance between feature exploration and exploitation, thus producing more robust and effective deepfake detection models. This bio-inspired strategy paves the way for developing adaptive deep learning systems that are finely tuned to address the nuanced challenges posed by the varied nature of digital forgeries in modern digital forensics.