Harnessing Machine Learning for Discerning AI-Generated Synthetic Images

TL;DR

This paper develops and tests deep learning models, especially DenseNet, to accurately distinguish AI-generated synthetic images from real ones, achieving high accuracy and improving digital media authenticity verification.

Contribution

It introduces the CIFAKE dataset and applies transfer learning with advanced architectures to enhance synthetic image detection, outperforming traditional methods.

Findings

DenseNet achieved 97.74% accuracy

Deep learning models outperform traditional methods

Transfer learning improves detection precision

Abstract

In the realm of digital media, the advent of AI-generated synthetic images has introduced significant challenges in distinguishing between real and fabricated visual content. These images, often indistinguishable from authentic ones, pose a threat to the credibility of digital media, with potential implications for disinformation and fraud. Our research addresses this challenge by employing machine learning techniques to discern between AI-generated and genuine images. Central to our approach is the CIFAKE dataset, a comprehensive collection of images labeled as "Real" and "Fake". We refine and adapt advanced deep learning architectures like ResNet, VGGNet, and DenseNet, utilizing transfer learning to enhance their precision in identifying synthetic images. We also compare these with a baseline model comprising a vanilla Support Vector Machine (SVM) and a custom Convolutional Neural Network (CNN). The experimental results were significant, demonstrating that our optimized deep learning models outperform traditional methods, with DenseNet achieving an accuracy of 97.74%. Our application study contributes by applying and optimizing these advanced models for synthetic image detection, conducting a comparative analysis using various metrics, and demonstrating their superior capability in identifying AI-generated images over traditional machine learning techniques. This research not only advances the field of digital media integrity but also sets a foundation for future explorations into the ethical and technical dimensions of AI-generated content in digital media.