International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
A Study of Image Tampering Detection Sreepriya S1, Dr.Asha T.S2 1PG
Student, Dept. of ECE,NSS College of Engineering, Kerala, India Dept. of ECE, NSS College of Engineering, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Professor,
Abstract - Images and videos have evolved into the primary data transporters in the modern era. The simplest video in TV news is frequently acknowledged as a confirmation of the accuracy of the reported news. Similarly, video observation and recordings can be used as primary trial material in a formal courtroom. Along with undeniable benefits, the availability of advanced visual media has a significant disadvantage. Image processing experts can undoubtedly access and alter image content in such a way that its significance is preserved. Outwardly noticeable follows are lost. Furthermore, with the ease of access to editing tools, the craft of modifying, and Forging visual substance is no longer confined to specialists. As a result, image manipulation for malicious purposes has increased. Digital forensics is the process of discovering and translating electronic data. The procedure's goal is to detect any proof in its most basic structure whereas conducting an organized examination by gathering, distinguishing, and approving computerized data for the purpose of procreating past affirmation. Forgery detection methodologies are typically classified into two types: active forensics and passive forensics, with digital watermarking and digital signatures being examples of active techniques. In contrast to these approaches, passive image forensics techniques work in the absence of a watermark or signature. These methods are based on the idea that, while digital forgeries may leave no visible signs of tampering, they may alter the underlying statistics of an image. The set of image forensic tools can be divided into five categories: 1) pixel-based methodologies for detecting statistical anomalies introduced at the pixel level, 2) format-based techniques that take advantage of statistical correlations introduced by a specific lossy compression scheme,3) camera-based methods that take advantage of artifacts introduced by the camera lens, sensor, or on-chip post processing, 4) physically-based techniques that explicitly model and detect anomalies in the three-dimensional interaction of physical objects, light, and the camera, and 5) geometric-based techniques that make measurements of objects in the world and their positions relative to the camera.
the viewer since the invention of photography. Originally a difficult task requiring many hours of work by a professional technician, the advent of digital photography has made it possible for anyone to easily modify images, and even easier to achieve professional-looking results. This has resulted in far-reaching social issues ranging from the veracity of images reported by the media to the doctoring of photographs of models in order to improve their appearance or body image. The advancement of photo manipulation techniques is a mixed blessing. On one hand, it facilitates the beautification of image and thereby encourages human beings to explicit and proportion their thoughts on visual arts of image editing; On the other hand, it is a great deal less difficult to forge the content of a given photo without leaving any seen clues and for this reason helps forgers to deliver fake information. Image retouching, image splicing, and copy and move attacks are all examples of image forgery. Image retouching is regarded as the least harmful type of digital image forgery. Image splicing is a simple process that allows you to copy and paste regions from different sources. This technique is known as paste-up, and it is formed by adhering images together using digital tools. This technique involves combining two or more images to create a fake image. One of the most common and difficult image tampering techniques is the copy and move attack. It was necessary to use the cover portion of a similar image to add or remove the information. The goal of a copy and move attack is to conceal some information in the original image. It is extremely difficult to distinguish a forged image from an original. The human eye cannot draw a distinction between a tampered region from a forged image. Typically, image forgery detection techniques encompass JPEG quantization tables, Chromatic Aberration, Lighting, Camera Response Function(CRF), Bicoherence and higher-order statistics, and Robust matching. The digital cameras encode the images based on JPEG compression, which configures the devices at various compression levels. Then, the sign of image tampering is evaluated by analyzing the inconsistency of lateral chromatic aberration. In which, the average angular between the local and global parameters is computed for every pixel in the image. If the average value exceeds the threshold, it is stated that the deviation in the image is unpredictable due to image forgery. The inconsistencies and the illuminating light source are then detected for each object in the image to identify the forgery . Various measurements, such as infinite, local, and multiple, are typically used to calculate the error rate. The CRF is then
Key Words: Image tampering detection, Image tampering localization, journal, Image forgery detection, Image forensics.
1. INTRODUCTION Individuals and organizations have frequently sought ways to manipulate and modify images in order to deceive
© 2022, IRJET
|
Impact Factor value: 7.529
|
ISO 9001:2008 Certified Journal
|
Page 903