IMAGE FORGERY DETECTION BASED ON FUSION OF LIGHTWEIGHT DEEP LEARNING MODELS

Abstract

Capturing images has been increasingly popular in recent years, owing to the
widespread availability of cameras. Images are essential in our daily lives because
they contain a wealth of information, and it is often required to enhance images to
obtain additional information. A variety of tools are available to improve image
quality; nevertheless, they are also frequently used to falsify images, resulting in the
spread of misinformation. This increases the severity and frequency of image
forgeries, which is now a major source of concern. Numerous traditional techniques
have been developed over time to detect image forgeries. In recent years,
convolutional neural networks (CNNs) have received much attention, and CNN has
also influenced the field of image forgery detection. However, most image forgery
techniques based on CNN that exist in the literature are limited to detecting a specific
type of forgery (either image splicing or copy-move). As a result, a technique capable
of efficiently and accurately detecting the presence of unseen forgeries in an image is
required. In this paper, we introduce a robust deep learning based system for
identifying image forgeries in the context of double image compression. The
difference between an image‟s original and recompressed versions is used to train our
model. The proposed model is lightweight, and its performance demonstrates that it is
faster than state-of-the-art approaches. The experiment results are encouraging, with
an overall validation accuracy of 92.23%.