Welcome to this week’s Tip Tuesday! Today we are showing you an interesting, perhaps a bit hidden, functionality of Amped Authenticate. There is no need to introduce the PDF file format: it is surely the most widespread format for sharing digital documents. Therefore, it may easily happen that you have to deal with a PDF containing images and that some of them get questioned.

What would you do in such a case (after realizing that if you drag-and-drop the PDF into Amped Authenticate, nothing happens, because it’s not an image file)? Would you take a screen capture of the picture and work with that? “Nooo!” – I hope you said! Screenshots are evil for image forensics! Ok, you would get some pixels to work with, but you would lose all metadata, you would lose encoding properties, you could be recompressing data… That means you cannot check image integrity, and finding manipulations becomes much harder. No, there’s a better way of dealing with images embedded in PDFs, and we’ll show you how!

Amped Authenticate users know how important it is to understand the processing history of an image, and they (hopefully!) know that “processing history” does not mean just splicing.
For example, there are cases where the image has been scaled or re-compressed, and
when one of these happen you should be aware of it, as they bring important consequences to the rest of your investigation.

Amped Authenticate offers many tools for processing history analysis under the Global Analysis filter category. Some of these, for example the DCT Plot, the Correlation Plot, and the JPEG Ghost Plot are… plots! They should be examined carefully, because we know that artifacts like a “comb-shaped” DCT histogram strongly suggests double JPEG compression, and so does a JPEG Ghost Plot with multiple local minima. The problem is… sometimes it’s just hard to see these artifacts, because they are “hidden” in the plot!

Consider the image below: at a first glance, its DCT Plot for DCT Frequency 4 seems rather “smooth”, and you could easily overlook it.

Clone detection (aka “copy-move detection”) is a very important image authentication task. Clones are a special case of image manipulation where part of an image is copied, possibly resized, rotated, sheared, etc., and then pasted to another region of the same image. The two main applications of cloning are:

• creating multiple (fake) copies of an object through copy-paste;
• removing an object from the scene by covering it with a cloned portion of the background.

This is explained with a very simple example in the image below.

The image forensics research community worked hard to develop techniques for clone detection, and two main approaches have been invented: block-matching and keypoint-matching. As suggested by their names, they are based on two different strategies, briefly explained below.

Which one is better? It depends, and we try to explain why with the table below:

So, if your question was: “Do I need a block- or a keypoint– based algorithm for my analysis?”, the answer is: you need both!

That’s why Amped Authenticate features both algorithms under the Local Analysis category: Clones Keypoints and Clones Blocks. Let’s compare their output on the sample image we used in this article:

We see that the cloned seagull (top row) is detected by the Clones Keypoints despite the strong down-scaling applied to the cloned object; such a geometrical transformation is too strong to be detected by Clones Blocks. On the other hand, Clones Blocks successfully detects the cloned background (bottom row), that is not detected by Clones Keypoints because the cloned area is just too flat and it does not contain enough keypoints.

We hope you enjoyed this quick tip! Stay tuned and don’t miss our next #ampedtiptuesday post!