Exposing Computer Generated Images by Using Deep Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach using convolutional neural networks and transfer learning to detect computer-generated images, achieving high accuracy without manual feature extraction, thus improving stability and efficiency.

Contribution

The authors develop CNN-based models that directly analyze raw pixels for image authenticity detection, outperforming existing methods by reducing manual feature engineering.

Findings

Achieved 97% accuracy comparable to state-of-the-art methods.

Provided more stable results with lower variance.

Eliminated manual feature extraction process.

Abstract

The recent computer graphics developments have upraised the quality of the generated digital content, astonishing the most skeptical viewer. Games and movies have taken advantage of this fact but, at the same time, these advances have brought serious negative impacts like the ones yielded by fakeimages produced with malicious intents. Digital artists can compose artificial images capable of deceiving the great majority of people, turning this into a very dangerous weapon in a timespan currently know as Fake News/Post-Truth" Era. In this work, we propose a new approach for dealing with the problem of detecting computer generated images, through the application of deep convolutional networks and transfer learning techniques. We start from Residual Networks and develop different models adapted to the binary problem of identifying if an image was or not computer generated. Differently from the current state-of-the-art approaches, we don't rely on hand-crafted features, but provide to the model the raw pixel information, achieving the same 0.97 of state-of-the-art methods with two main advantages: our methods show more stable results (depicted by lower variance) and eliminate the laborious and manual step of specialized features extraction and selection.