An Improved Hybrid Deep Learning Strategy to Predict Digital Image Forgery using Artificial Intelligence Principle

Several modern-day application domains rely heavily on digital images. The practice of manipulating a digital image in such a way as to conceal important or useful details is known as image faking. The new researchers will benefit from this study’s information on picture fraud and how to identify it. An image or picture that is digitally represented is called a digital image. A two-dimensional image is represented numerically, typically in binary form. One of the most important ways to disseminate information on the Internet, digital images are employed in practically every industry. National security and commercial secrets make up the vast majority of digital photographs. Scientists and technologists now face the pressing problem of how to ensure the integrity of images due to the proliferation of the Internet and other forms of simple multimedia sharing. Image processing may be defined as a method for improving or extracting useful information from digital images by converting them to a digital format and then doing various operations on them. Many different types of researchers are interested in image processing. In order to create a featured picture that can be used to train a pre-trained model, the difference between the original and compressed versions of the image must be calculated. Training the pre-trained model involves removing the classifier and replacing it with a newly-tuned one. Eight distinct pre-trained models optimized for binary classification are compared. Applying the suggested strategy with the modified pre-trained models outperforms current state-of-the-art approaches, according to the experimental results. For the purpose of easily identifying forgery images, this paper presented a new AI-assisted approach called AIIFI. To assess the efficacy of the proposed scheme, it was cross-validated with an existing learning model called Convolution Neural Network (CNN).