A Study of Original and Tempered Images for Real-Fake Image

Abstract

The widespread use of image editing technologies in the digital age has raised concerns about the authenticity of visual content. This study delves into the field of image forensics, specifically analyzing original and tempered photos to determine their graphical behavior. The major goal is to develop solid algorithms for distinguishing between authentic and fraudulent photos based on an in-depth assessment of their visual properties. The study makes use of a large data collection that includes both original and manipulated photographs from a variety of sources and contexts. To reveal small differences between authentic and modified pictures, image processing techniques such as noise analysis, color profile investigation, and geometric feature extraction are used. Machine learning algorithms are critical in automating the analysis process and increasing the efficiency and scalability of the proposed methodology. Picture security is an issue for every company that employs digital images. Suspect data has long been used in forensics and public safety pictures, images from crime scenes, biometric photos, and other types of images. In this discipline, the usage of digital photographs has increased dramatically with the advancement of digital imaging. Digital image processing has made picture manipulation easier, but it has also aided in the development of several novel techniques in forensic investigation. Digital picture authenticity is becoming an issue due to the public availability of several programs for cropping and manipulating images. It serves as compelling evidence in many different types of crimes write for a variety of purposes. This development is picture processing or edits of two are also simplifies and editing photos. The most typical kinds of Conducted.