Exposing photo manipulation with inconsistent reflections

O’Brien, James F.; Farid, Hany

doi:10.1145/2077341.2077345

Cited by 87 publications

(35 citation statements)

References 42 publications

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“…Geometry-based methods focus at detecting inconsistencies in light source positions between specific objects in the scene [5]- [11]. Color-based methods search for inconsistencies in the interactions between object color and light color [2], [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Exposing Digital Image Forgeries by Illumination Color Classification

Carvalho

Rieß

Angelopoulou

et al. 2013

IEEE Trans.Inform.Forensic Secur.

305

173

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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.

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Section: Related Workmentioning

confidence: 99%

Exposing Digital Image Forgeries by Illumination Color Classification

Carvalho

Rieß

Angelopoulou

et al. 2013

IEEE Trans.Inform.Forensic Secur.

305

173

View full text Add to dashboard Cite

show abstract

“…They detected illumination inconsistencies of an image by extracting edge or text-based features. If the image file under consideration carried information about image type, camera model, and motion after being captured, the data was found to be helpful for preventing any image forgery attempts by making the latter a difficult job [10]. However, detection of reflection-based forgeries is not a trivial task.…”

Section: Related Workmentioning

confidence: 99%

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

Bhatti¹,

Hussain²,

Qayyum³

et al. 2018

ijacsa

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Abstract-Digital images are a very popular way of transferring media. However, their integrity remains challenging because these images can easily be manipulated with the help of software tools and such manipulations cannot be verified through a naked-eye. Although there exist some techniques to validate digital images, but in practice, it is not a trivial task as the existing approaches to forgery detection are not very effective. Therefore, there is need for a simple and efficient solution for the challenge. On the other hand, digital image steganography is the concealing of a message within an image file. The secret message can be retrieved afterwards by the author to check the image file for its veracity. This research paper proposes Sabiomha, an image forgery technique that make use of image steganography. The proposed technique is also supported by a software tool to demonstrate its usefulness. Sabiomha works by inserting an invisible watermark to certain alpha bits of the image file. The watermark we have used to steganograph an image is composed of a combination of text inputs the author can use to sign the image. Any attempts to tamper the image would distort the sequence of the bits of the image pixel. Hence, the proposed technique can easily validate originality of a digital image by exposing any tampering. The usability of our contribution is demonstrated by using the software tool we developed to automate the proposed technique. The experiment which we performed to further validate our technique suggested that Sabimoha could be flawlessly applied to image files.

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“…Quite the opposite, dichromatic-based methods can successfully handle consistently colored surfaces but cannot be applied to highly textured surfaces, as they need precise color segmentation. R. Tan, K. Nishino, and K.Ikeuchi [11] introduces a single integrated scheme to estimate illumination chromaticity from single colored and multicolored surfaces and require only uneven highlight region without segmenting the colors inside them. This technique gives relationship between illumination chromaticity and image chromaticity.…”

Section: Physics-based Inverse-intensity Chromaticity Space Estimatesmentioning

confidence: 99%

“…In geometrybased schemes center of attention is on detecting inconsistencies in light source spots between particular objects in the picture [5]- [11] . Color-based schemes search for inconsistencies in the interactions between object color and light color [2], [12], [13] .…”

Section: Review Of Digital Image Forgery Detectionmentioning

confidence: 99%