Classifying Real vs Fake Images
Table of contents:
- Introduction
- Detecting Fake Images
- Deep Learning and Neural Networks
- The Dataset
- Pre-trained Models
- Model Evaluation and Improvement
- Footnotes
Introduction
In today’s digital age, the rise of sophisticated image generating tools have made it much more difficult to discern between real and fake images. That being said, there still remains some hope amidst this chaos, and that is Artificial Intelligence! With its ability to analyze vast amounts of data and detect patterns, AI has emerged as a powerful tool to combat these “fake images”. In this blog post, we will explore how AI can be used to classify real versus fake images, helping us navigate the continuously changing landscape of the digital age 1.
Detecting Fake Images
In order to succesfully detect “fake images”, AI algorithms need to be trained to detect and analyse various patterns and characteristics of an image. These characteristics may include examining inconsistencies in lightings, shadows and reflections etc. By comparing an image against a reference database of known real images, AI algorithms can identify suspicious patterns and flag potentially fake images.
Deep Learning and Neural Networks
Deep learning has proven to be highly effective in image classification tasks (as seen with the animal classifier in my last post). Convolutional Neural Networks are particularly well-suited for image classification tasks. By training a CNN on a large dataset of both real and fake images, it can learn to recognise patterns and features that distinguish between the two categories: real or fake.
The Dataset
Building a robust dataset is extremely important for training an AI model to classify real versus fake images accurately. It is also essential that the dataset covers a variety of synthetically generated images. Additionally, the dataset needs to be accurately labeled to help train the AI model effectively. Thankfully, in my case, CIFAKE is an existing dataset that contains 60,000 of each real and fake images. This dataset will be used to help develop an AI model to hopefully be able to distinguish between real and fake images 2.
Pre-trained Models
Training an AI model from scratch can take up a lot of time and resources. However, using pre-trained models can significantly speed up the process dramatically (similar to the animal classification example in my previous post). Pre-trained models such as ResNet have been trained on large-scale datasets and have learned to extract general features from images. By fine-tuning these models using a dataset of real and fake images, one can hopefully utilize its efficiency and adapt them to the specific task of classifying real vs fake images.
Model Evaluation and Improvement
AI models for image classification will require constant evaluation and improvement. Regularly re-evaluating the model’s performance on a validation dataset can help identify areas where the model may be inaccurate. By continuously refining the model and updating it with new examples of fake images, we can further improve its accuracy and ability to classify such images.