Using ensemble models to detect deepfake images of human faces

Abstract

Deepfakes, synthetic media generated through advanced artificial intelligence techniques, are a rising threat to content authenticity. This paper explores deepfake detection methods to discern manipulated media from genuine content. We evaluate two convolutional neural network (CNN) architectures - EfficientNetB4 and EfficientNetB4 with attention mechanisms - on the Forensics++ and DeepFake Detection Challenge datasets. Our key contributions are: 1) Demonstrating that integrating attention enhances model performance, with EfficientNetB4 attention achieving superior accuracy over baseline EfficientNetB4 in both intra-dataset and cross-dataset scenarios; 2) Elucidating attention’s efficacy in improving deepfake detection by concentrating on manipulated regions. Our experiments highlight attention’s potential in advancing state-of-the-art deepfake detection. As deepfakes grow increasingly realistic, robust techniques like attention become imperative for multimedia forensics. This paper provides valuable insights toward developing adaptable deepfake detection systems to preserve content integrity.