CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images

• URL: http://arxiv.org/abs/2303.14126v1
• Date: Fri, 24 Mar 2023 16:33:06 GMT
• Title: CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
• Authors: Jordan J. Bird, Ahmad Lotfi
• Abstract summary: This article proposes to enhance our ability to recognise AI-generated images through computer vision. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake.
• Score: 7.868449549351487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognise AI-generated images through computer vision. Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs. The model is capable of generating complex visual attributes, such as photorealistic reflections in water. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study then proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake. Following hyperparameter tuning and the training of 36 individual network topologies, the optimal approach could correctly classify the images with 92.98% accuracy. Finally, this study implements explainable AI via Gradient Class Activation Mapping to explore which features within the images are useful for classification. Interpretation reveals interesting concepts within the image, in particular, noting that the actual entity itself does not hold useful information for classification; instead, the model focuses on small visual imperfections in the background of the images. The complete dataset engineered for this study, referred to as the CIFAKE dataset, is made publicly available to the research community for future work.

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