How to Evaluate an Image?
It’s very simple, you must observe every image very carefully. We have total 100 images for evaluation (selected with different group). Every image has slicer with eleven possible answers. Answer options have 0 to 1 range (like 0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9 and 1)
If you think this image has definitely some legit or forgery or some changes then you need to click on 1 or if you think it has some partial changes then you can select other option like (0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9) and if you think it’s not fake or its original then click on 0 (0 means original/no fake).
Our project name is Social truth. During the last decade, there has been a significant revolution in people’s socialization. From the early days to today, there are different types of Social media established. Facebook like any other social media, people have been accepting these new forms of socialization. Social networks, media and platforms are becoming the usual way of communication and information exchange.
Most people like to share their images through social media. Generally, people do it for fun, but it makes offense when concealed an object or changed someone’s face within the image. Before questioning someone’s intention, it is important to identify the intrinsic difference between authentic images and tampered images. Every social media platform now concentrated on tampered images identification model sand techniques. For this reason, it is important to train identify models with the proper tampered dataset.
There are various datasets available in tampered images, but no dataset contains every tampered technique. Our target to provide a powerful tempered dataset with all possible tempered techniques. This Web evaluation is a part of Social truth Project. There are different kind of images included with various tempering model and resolution.
This study will provide an overview of typical image tampering types and will release a new dataset that included all possible tampering detection approaches. Hopefully, this project encourages the research community for further study related to forgery image detection.
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