Complementary Attention-Based Deep Learning Detection of Fake Faces
Supriyo Sadhya, Xiaojun Qi · 2023
The access to large-scale public databases along with the fast progress of deep learning techniques have led to the generation of very realistic fake content. This has raised significant concerns because of their use in social media and the generation of fake news. Thus, the detection of such manipulations has become an increasingly important research area, especially the detection of fake faces has become very important in the field of digital forensics. This paper presents a complementary attention-based deep learning system to detect fake faces. This system effectively incorporates our proposed simple Layer-Integrated Channel Attention (LICA) and Scaled Spatial Attention (SSA) mechanisms in VGG network architecture to capture the importance along each channel and at each spatial location to distinguish between real and manipulated faces and improve detection performance. Our extensive experimental results demonstrate that the proposed system outperforms the state-of-the-art system in detecting fake faces generated by each of the four commonly used manipulations including entire face synthesis, identity swap, attribute manipulation, and expression swap in terms of both accuracy and Area Under Curve (AUC) metrics. It also achieves better performance than the state-of-the-art system to detect fake faces generated by any of the four aforementioned manipulations.