web evaluation of deep fake image project

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Genuine/Forgery Image Evaluation

Please give your opinion on each image. You need to evaluate whether each image is Genuine or Forgery using perception criteria. You should evaluate 10 images (selected from different groups).

  • If you think the image is Genuine, you should select 0
  • If you think the image is Forgery, you should select 1
  • If you think the image has some partial changes, then you can select a value in the range of 0.1- 0.9
DEEP IMAGE EXAMPLE
Image Evaluation

Project Introduction

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.