Digital image forgery detection on artificially blurred images

I just want to discuss about some of the very basic steps involved in Image forgery detection with OpenCV code snippets. We all know the idiom "A picture is worth a thousand words". So we need to make sure that, whatever the picture says is correct. We are very much interested in posting the images of ourselves as well as  friend’s. But these sharing can be dangerous also. With the advancement in image editing technologies, many users have become victims of digital forgery as their uploaded images were forged for fraudulant activities. 

Steps to detect Image forgery: These are the very simple steps to detect forgery in images.

Input Image->pre-processing->Calculation of image gradient direction-> Calculation of average gray value->Edge pixel width calculation->Elimination of thin edges->Apply center of gravity after dividing into nine blocks->Highlight the forged object.

Now we can go little more deeper into all these steps.

Pre-processing:   For doing any analysis we need to do pre-processing. This is like repairing the data to fit for our analysis. The steps depends on our analysis.

RGB to gray scale conversion. 

Convolve the image horizontally and vertically 

              Convolution filtering is used to modify the spatial frequency characteristics of an image.

             Here we can familiar with one technical word, called Kernel. It is nothing but a small matrix of numbers 

             which is used for image convolution. Generally used Kernel matrix size is 3x3.

Calculation of image gradient direction: 

Calculation of average gray value: Corresponding to each zone, low medium and high intensity values are calculated.

  // Calcluating theta to divide the gradient direction into 8 zones

   etc..

Finding edge pixels: When the pixels located in the edge of the area, along the gradient direction θ(i, j), it will have a high gray value which is greater than the middle gray value. The middle gray value is greater than the low gray value [1]. IH − IM > α ∩ IM − LL > α 

             The pixels that satisfy the given equation are defined as edge pixels and it is marked as white pixel

Edge pixel width calculation: Make the edge pixels continuous by adding one more pixels into it.

 Elimination of thin edges: From our thorough study, we concluded that normal images have edge width less than five. So the edge width values less than five are neglected and greater than five are retained.

Apply center of gravity after dividing into nine blocks: Divide the images into nine blocks of having same size. Then apply center of gravity. 

Highlight the forged object: The forged object is highlighted by applying flood fill algorithm.