Exposing Digital Image Forgeries by Illumination Color Classification

Abstract

Abstract-For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learningbased and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics-and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture-and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.Index Terms-Color constancy, illuminant color, image forensics, machine learning, spliced image detection, texture and edge descriptors.

Using Sab-Iomha for an Alpha Channel based Image Forgery Detection

“…Digital content is used as an effective way of communication among different stakeholders [5]. The advent of digital devices and communication technologies has led to increase in the use of image files for sharing visual moments and photographs.…”

“…Image tampering is a known handling technique [5]. Deception of typical image files is relatively a tedious task and requires sufficient expertise.…”

“…Lighting, inconsistent shading, and shadows have been used as a method for collecting evidence on image forgery [5]. Mixture of shadow and shading was rationally used to serve for the purpose and both were made dependent on each other but in case they are not, the corresponding image is found to be a tampered one.…”

Precision Statistical Analysis of Images Based on Brightness Distribution

“…These characteristics will be different for the images taken in different configurations. These inconsistencies can be analyzed to detect the altered regions [3], [4].…”

Expose Spliced Photographic Basing on Boundary and Noise Features

“…Fridrich [4] estimates the parameters of the noise model from the original camera, and then calculates the correlations between the estimated camera noise and the extracted image noise to authenticate an image. Other effective features to expose forgery include inconsistencies in the illumination [5], light direction, anomalous height ratio of two objects in an image [6], etc.…”