Discerning Reality: A TensorFlow Approach to Classifying AI-Generated Images

Sammanfattning

The rise of artificial intelligence (AI) in creating digital images brings both opportunities and challenges. On one hand, AI democratizes content creation, enabling people without specialized skills to produce realistic images. On the other hand, it raises concerns about authenticity, copyright, and misinformation. As AI-generated images become increasingly hard to distinguish from real ones, developing reliable methods to tell them apart becomes crucial. Our research question centers on assessing the efficacy of Keras-based models within TensorFlow in distinguishing between photorealistic AI-generated images and authentic ones. We aim to explore the capabilities of these models while identifying the specific challenges and potential advancements in utilizing Keras to enhance digital authenticity. Our methodology includes designing the model’s architecture, selecting a diverse dataset of photorealistic AI-generated and real images, and applying iterative training and optimization techniques. The performance of the model is assessed through a combination of quantitative metrics and qualitative analysis. Quantitative evaluation includes key metrics such as accuracy, precision, and recall, providing precise measurements of the model’s ability to classify images accurately. The research utilizes a dataset of 120,000 images, drawn from the CIFAR-10 dataset and synthetic images generated by Stable Diffusion 1.4. This dataset is carefully divided into training and testing sets to ensure a balanced exposure to both classes of images, reducing bias in the model’s learning process and enhancing the reliability of our findings. The results were promising, with the model attaining a training accuracy of 99.12% and exhibiting high precision and recall values, indicative of its capacity to capture and analyze the nuances of complex visual features. On the validation set, the model maintained a robust accuracy of 94.87%. Despite a slight precision-recall gap, these results affirm the model’s effectiveness in accurately classifying images and bolster confidence in its application as a tool for maintaining the integrity of digital media.