Incorporating Ensemble and Transfer Learning For An End-To-End
  Auto-Colorized Image Detection Model
Ahmed Samir Ragab Shereen Aly Taie Howida Youssry Abdelnaby
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.