Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model

TL;DR

This paper introduces a novel ensemble and transfer learning-based model for detecting colorized images, achieving high accuracy and outperforming existing methods in distinguishing natural from manipulated images.

Contribution

It combines transfer learning with ensemble techniques using multiple pre-trained networks to improve detection accuracy and reduce training resources.

Findings

Accuracy ranged from 94.55% to 99.13%.

Outperformed existing state-of-the-art models.

Achieved low Half Total Error Rate values.

Abstract

Image colorization is the process of colorizing grayscale images or recoloring an already-color image. This image manipulation can be used for grayscale satellite, medical and historical images making them more expressive. With the help of the increasing computation power of deep learning techniques, the colorization algorithms results are becoming more realistic in such a way that human eyes cannot differentiate between natural and colorized images. However, this poses a potential security concern, as forged or illegally manipulated images can be used illegally. There is a growing need for effective detection methods to distinguish between natural color and computer-colorized images. This paper presents a novel approach that combines the advantages of transfer and ensemble learning approaches to help reduce training time and resource requirements while proposing a model to classify natural color and computer-colorized images. The proposed model uses pre-trained branches VGG16 and Resnet50, along with Mobile Net v2 or Efficientnet feature vectors. The proposed model showed promising results, with accuracy ranging from 94.55% to 99.13% and very low Half Total Error Rate values. The proposed model outperformed existing state-of-the-art models regarding classification performance and generalization capabilities.