Detection of Forgeries in Images: A Survey Abstract: While the technology to detect forgeries in images is able to detect images even at complex images, this well is only limited to known forgery methods. It trains neural networks from large amounts of original and corresponding forged images created with known techniques. But it fails to process unseen forgery techniques. One such proposed solution to this problem, recently, is to employ a hand-crafted generator of forged images to generate a series of fake images and feed them to the neural network for training However, the aforementioned approach has certain limitations detecting performance in situations where the hand-craft generator has not taken into consideration invisible forging processes. In this study, we use a meta-learning approach to create a highly adaptive detector for detecting novel forging techniques, overcoming the drawbacks of current approaches. By employing meta-learning approaches to train a forged image detector, the suggested method allows the detector to be fine-tuned using a small number of fresh forged examples. In order to detect forged images with comparable characteristics, the suggested method inputs a limited number of the forged images to the detector and allows the detector to modify its weights based on the statistical properties of the input forged photos. With IoU gains ranging from 35.4% to 127.2%, the suggested approach significantly improves forgery method detection. These findings illustrate that the suggested approach outperforms the state-of-the-art techniques in the majority of situations and greatly enhances detection performance with a relatively small number of samples.
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