Automated Detection of Manipulated Images Using Deep Learning and Computer Vision Techniques

In the current era, where image manipulation techniques have developed at a faster pace, detection of altered images using the conventional methods has become increasingly challenging. This article presents a framework for automatically detecting the altered images using deep learning and computer vision techniques. The system focuses on providing methods to detect various image manipulations such as copy-move, inpainting and image-slicing. With the help of integrating convolutional neural networks (CNN) with anomaly detection methods and image preprocessing, the proposed framework can successfully find visual image anomalies that show potential image manipulation. The experiment conducted shows that the framework could effectively detect image alterations using the state-of-the-art computer vision and deep learning algorithms.