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. The 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 performance, 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). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The 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). The 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. The experimental results established the proposed model's superiority over recently developed approaches.
Highlights
The extension of Internet services and the strengthening and proliferation of social networks such as Reddit, Facebook, and Instagram had had an important effect on the number of content prevailing in digital media
The quality of false images rises and it seems to be original images. Postprocessing manipulations, such as brightness equalization/changes and JPEG compression, might decrease the traces left by manipulation and make it very complex to identify [3]. e copy-move forgery detection (CMFD) consists of deep learning- and hand-crafted-based approaches. e previous one is largely separated into hybrid, block, and key point-based methods and employs convention framework from fine-tuned/scratch algorithms
DLFM-CMDFC technique comprises the fusion of generative adversarial network (GAN) and densely connected network (DenseNet) models
Summary
The extension of Internet services and the strengthening and proliferation of social networks such as Reddit, Facebook, and Instagram had had an important effect on the number of content prevailing in digital media. Us, there are various attempts to develop a preprocessing layer for enhancing the strength of feature extraction [6] and combine several detector-based likelihood maps and individual CNN-based consistency maps for improving the solution of tampering location. In few conventional CNN-based forensic detectors is usually not real world for several details, for example, by means of strength in feature extraction and solution of tampering position. Still, they endure numerous limits in the abovementioned methods. DLFM-CMDFC technique comprises the fusion of generative adversarial network (GAN) and densely connected network (DenseNet) models
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