Advanced 3D CNN Techniques For Robust Face Forgery Detection

Abstract

Researchers are currently investigating sophisticated methods for face forgery detection in response to the escalating worries regarding the simplicity and effectiveness of existing face forging techniques, in an effort to prevent potential misuse of this technology. A wavelet dual-branch network is one method now in use for predicting and recognizing modified faces. But even if this system works well in some situations, it can still have errors, which would reduce its overall dependability. Researchers have presented a fresh solution to this problem by integrating the 3D Convolutional Neural Network (3DCNN) algorithm into the framework for facial forgery detection. The purpose of integrating 3DCNN is to improve prediction accuracy and get beyond the drawbacks of the wavelet dual-branch network. 3DCNN, in contrast to conventional 2D convolutional networks, considers the temporal dimension, enabling it to capture spatiotemporal features in the data. The modified approach delivers better prediction performance by utilizing 3DCNN's capabilities, especially in situations where fake faces may have subtle or dynamic modifications. The algorithm's enhanced sensitivity to minute details stems from its capacity to assess volumetric data in both geographical and temporal domains, making it a more robust and dependable face forgery detection method. This development emphasizes the significance of continuously improving detection approaches in the face of technology improvements and represents a significant step forward in the ongoing attempts to keep ahead of growing face forgery techniques.