Fake Face Image Classification by Blending the Scalable Convolution Network and Hierarchical Vision Transformer

Abstract

A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword “image processing” capabilities to deceive innocent users. The underlying technology is based on advanced machine learning and deep learning algorithms. The intentions of cyber criminals range from simple mimicking or trolling to creating violent situations in society. Hence, it is necessary to resolve such problems by identifying the fake face images generated by expert humans or artificial intelligent algorithms. Machine learning and artificial neural networks are used to resolve the issue. In this work, we have designed an approach for detecting deep learning-generated fake face images by combining the capabilities of the scalable convolutional neural networks (CNN) “EfficientNet” and hierarchical vision transformer (ViT) “shifted window transformer”. The proposed method accurately classifies the fake face images with a 98.04% accuracy and a validation loss of 0.1656 on the 140 k_real_fake_faces image dataset.