Abstract:
Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery
detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move
approach. e standard image processing methods physically search for patterns relevant to the duplicated material, restricting
the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved per formance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for
good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for
detecting and localizing copy-move forgeries (DLFM-CMDFC). e proposed DLFM-CMDFC technique combines models of
generative adversarial networks (GANs) and densely connected networks (DenseNets). e two outputs are combined in the
DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine
(ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm
algorithm (AFSA). e networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the
difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance.
e experimental results established the proposed model’s superiority over recently developed approaches.
