Utilizing deep learning models for accurate classification of authentic real-world images and ai-generated images

Abstract

In this paper, i present a study focused on effectively classifying images into two categories: real-world images and artificially generated images using deep learning techniques. To achieve this, i first gathered a public dataset named CIFAKE, which comprises both real images captured from the real world and synthetic images generated by artificial intelligence algorithms. I then developed a convolutional neural network (CNN) with a custom attention module tailored to enhance classification accuracy. Our approach involved comparing the performance of our custom CNN model with several state-of-the-art CNN models commonly used for image classification tasks. These models include VGG16, ResNet50, InceptionV3, MobileNet, and DenseNet. By evaluating these models on the CIFAKE dataset, I aimed to discern the effectiveness of our proposed architecture in distinguishing between real and AI-generated images. Furthermore, I conducted a thorough analysis of the results using various statistical measurements to assess the classification performance of each model. This analysis included metrics such as accuracy, precision, recall, and F1 score. Through these statistical analyses, I sought to provide insights into the strengths and limitations of different deep learning models for image classification tasks, particularly in distinguishing between real-world and AIgenerated images. Overall, our study contributes to the advancement of image classification techniques by addressing the increasingly relevant challenge of differentiating between authentic real-world images and synthetic images generated by artificial intelligence. The findings of this research could have significant implications for various applications, including image forensics, content moderation, and computer vision systems deployed in real-world scenarios.