Deep fake detection using an optimal deep learning model with multi head attention-based feature extraction scheme

Abstract

Face forgery, or deep fake, is a frequently used method to produce fake face images, network pornography, blackmail, and other illegal activities. Researchers developed several detection approaches based on the changing traces presented by deep forgery to limit the damage caused by deep fake methods. They obtain limited performance when evaluating cross-datum scenarios. This paper proposes an optimal deep learning approach with an attention-based feature learning scheme to perform DFD more accurately. The proposed system mainly comprises ‘5’ phases: face detection, preprocessing, texture feature extraction, spatial feature extraction, and classification. The face regions are initially detected from the collected data using the Viola–Jones (VJ) algorithm. Then, preprocessing is carried out, which resizes and normalizes the detected face regions to improve their quality for detection purposes. Next, texture features are learned using the Butterfly Optimized Gabor Filter to get information about the local features of objects in an image. Then, the spatial features are extracted using Residual Network-50 with Multi Head Attention (RN50MHA) to represent the data globally. Finally, classification is done using the Optimal Long Short-Term Memory (OLSTM), which classifies the data as fake or real, in which optimization of network is done using Enhanced Archimedes Optimization Algorithm. The proposed system is evaluated on four benchmark datasets such as Face Forensics + + (FF + +), Deepfake Detection Challenge, Celebrity Deepfake (CDF), and Wild Deepfake. The experimental results show that DFD using OLSTM and RN50MHA achieves a higher inter and intra-dataset detection rate than existing state-of-the-art methods.