Fake-checker: A fusion of texture features and deep learning for deepfakes detection

The evolution of sophisticated deep learning algorithms such as Generative Adversarial Networks has made it possible to create deepfakes videos with convincing reality. Deepfake identification is important to address internet disinformation campaigns and lessen negative social media effects. Existing studies either use handcrafted features or deep learning-based models for deepfake detection. To effectively combine the attributes of both approaches, this paper presents a fusion of deep features with handcrafted texture features to create a powerful fused feature vector for accurate deepfakes detection. We propose a Directional Magnitude Local Hexadecimal Pattern (DMLHP) to extract the 320-D texture features and extract the deep feature vector of 2048-D using inception V3. Next, we employ the Principal Component Analysis to reduce the feature dimensions to 320 for a balanced representation of features after fusion. The deep and handcrafted features are combined to form a fused feature vector of 640-D. Further, we employ the proposed features to train the XGBoost model for the classification of frames as genuine or forged. We evaluated our proposed model on Faceforensic + + and Deepfake Detection Challenge Preview (DFDC-P) datasets. Our method achieved the accuracy and area under the curve of 97.7% and 99.3% on Faceforensic + + , whereas 90.8% and 93.1% on the DFDC-P dataset, respectively. Moreover, we performed a cross-set and cross-dataset evaluation to show the generalization capability of our model.