Illusion Unveiler

Abstract: Fake image detection is an important problem in the field of computer vision and machine learning. as the use of manipulated images for deception or propaganda purposes is becoming increasingly common. In this, we propose a machine learning approach for detecting fake images, which is based on a combination of deep convolutional neural networks and traditional image processing techniques. Our method extracts a set of features from the input image, including statistical properties, color distributions, and texture information. These features are then fed into a classifier, which determines whether the image is genuine or manipulated. We evaluate our approach on a large data-set of read and fake images and demonstrate that it achieves state-of the art performance in terms of accuracy. precision, and recall. Our results suggest that machine learning methods can be effective for detecting fake images and have the potential to be used in a wide range of applications, including social media content moderation, news verification, and forensic analysis