We’ve just launched some pretty important additions to Amped Authenticate. Not only have we integrated it with CameraForensics, but we have also made some major improvements to the quantization tables in addition to many other internal improvements. Read below for the details.
The main purpose of Amped Authenticate is to verify if a picture is an original coming from a specific device or if it’s the result of manipulation using image editing software. One of the main tests to verify the file integrity is to acquire the camera that is assumed to be the one that has generated the photo (or at least the same model) and verify if the format is compatible with the file under analysis.
While this sounds easy in practice, many devices have so many different settings and because of this it can be challenging to recreate the same conditions. Furthermore, the camera is often not available.
What if we look on the web for pictures coming from a specific device? While we cannot, in general, guarantee the integrity of files downloaded from the web, we can triage them pretty easily and do a comparison with the image under analysis.
But how do you search for images on the web in an efficient manner? We have had “Search for Images from Same Camera Model…” in Authenticate for quite some time. It allows you to search on Google Images and Flickr, but the search is not always optimal, as it has to apply different workarounds to work efficiently in a forensic setting.
So, what if someone built a database of pictures on the web, optimized for investigative use, enabling you to instantly search for images coming from a specific device and with specific features such as resolution and JPEG quantization tables? Turns out the guys at CameraForensics did exactly this (and much more) and we partnered with them to provide a streamlined experience.
Let’s see how it works. Continue reading
We work in the field of forensic video analysis, which is generally intended as the analysis of the images themselves and their context in a legal setting. For this reason, our customers often ask us if our products are valid for court use and if they have been validated and certified. We have written this post as an answer to the most common questions related to this topic.
You can also download this as a PDF document here.
What are the scientific foundations of Amped Software products?
All the processes implemented in our software follow the principles of scientific methodology. Any process follows these basic principles:
- Accuracy (Reliability): our tools and training program help users avoid processing errors caused by the implementation of an inappropriate tool or workflow and help mitigate the impact of human factors / bias.
- Repeatability: the same process, executed by the same user at a different time, must lead to the same result. The project format in Amped FIVE, for example, does not save any image data. Every time a project is reopened, all the processing happens again starting from the original data. In the event that a project file is lost or as a part of a validation or other test scenario, the same user can repeat the steps and settings, guided by the tool’s report, and achieve the same results.
- Reproducibility: another user with the proper competency, should be able to reproduce the same results. Amped FIVE generates a complete report detailing all the steps of the processing, the settings / parameters applied, a description of the algorithms employed in the processing and the scientific references for those algorithms (when applicable). In this way, another user, with a different tool set or by implementing the same algorithms, should be able to reproduce the same results. Given the huge number of implementation details and possible differences, it is not expected to produce a bit by bit copy of the results, but only to produce an image of similar informative content.
Additionally, we apply strict due diligence on the applicability of the algorithms for the forensic environment. Not every algorithm is, in fact, properly applicable in a forensic science setting. We cannot use algorithms which have a random component because they would not be reproducible and repeatable (when we do, we set a fixed seed for the random number generation) and we cannot use algorithms which “add” external data to the original, for example improving the quality of a face with information added from an average face. All information is derived from the actual evidence file.
We employ algorithms which have been validated by the scientific community through peer review, such as university textbooks, scientific publications, or conference papers. If for some specific task, there are not good enough algorithms available or we need to adapt existing algorithms, we describe the algorithm and attempt to publish them in scientific journals. Continue reading
One of the things that fascinate me the most in forensic video analysis is the relation between the subjective digital data and the objective human interpretation involved in any investigation. Psychological biases and the fallacies of human perceptions easily verifiable with any of the popular optical illusions are just some of the factors which must be taken into account while doing investigations.
But this time I want to look at things from a higher level and talk about the usefulness of video as evidence and our perception of it. Chances are you have already seen the very interesting article: “The Value of CCTV Surveillance Cameras as an Investigative Tool: An Empirical Analysis” (link).
The abstract provides some impressive numbers: “This study sought to establish how often CCTV provides useful evidence and how this is affected by circumstances, analysing 251,195 crimes recorded by British Transport Police that occurred on the British railway network between 2011 and 2015. CCTV was available to investigators in 45% of cases and judged to be useful in 29% (65% of cases in which it was available).”
For reference, this is the decision workflow used in the classification (image from the above paper).
This really made me feel good. It looks like what we are doing here at Amped Software is having an impact on society, and more than we expected. I think most people in our community would be surprised by the numbers. At Amped, we see hundreds of cases every year, and for more than half of the images and videos that we receive, we just say that they are useless. Continue reading
Normally, we release new stuff for your desktop, but this time we released a little new section on our website to help you with the management of your Amped Software licenses and training: the Amped Support Portal.
In the portal, you can find all the essential information about your software licenses and the training classes that you have attended.
Just head to https://support.ampedsoftware.com and you will see a typical Email / Password login page.
If you are an Amped Software user and we have received your details when you purchased a license, you should already be able to log into the system. Just insert your email address and hit the button “Reset password”. Then check your email and follow the instructions to change the password and then log in. If you don’t receive an email, this means that we don’t have you in our records. But no worries, just send an email to email@example.com and we’ll create an account for you once we have identified your available licenses.
You can see all your licenses and their expiration dates, you can download the latest versions of the software, and you can generate license certificates that your administration or management may require.
Additionally, you will also see all the training classes that you have attended and download the related training certificate.
We plan to add additional features soon, for example, adding the possibility to receive email notifications before the expiration of your SMS (software updates). So, stay tuned!
As usual, feel free to contact us if you have any comments or suggestions about the new portal.
Following our previous posts (here and here) on the topic of unrealistic and unscientific enhancement, we’ve been asked by the folks at Forensic Focus to write a more in-depth article on the topic.
You can read the full article published on Forensic Focus here.
In the last few days, there’s been a lot of noise about the latest impressive research by Google. This is a selection of articles with bombastic titles:
The actual research article by Google is available here.
First of all, let me say that technically, the results are amazing. But this system is not simply an image enhancement or restoration tool. It is creating new images based on a best guess, which may look similar but also completely different than the actual data originally captured. Continue reading
I assume most of the readers of this blog are video / photo / gadget / phone / camera geeks. I am sure you didn’t miss the reviews of the latest Apple iPhone 7 Plus and Google Pixel phones. They have a lot in common, but there is one major aspect that is interesting for our applications: things are slowly moving from photography to computational photography. We are no longer just capturing light coming from optics and applying some minor processing to the pixel values to make the picture more pleasant to the viewer.
Phones must be slim and light and yet we still expect to have near DLSR quality. So, now computational photography comes into play. The iPhone 7 Plus, for example, uses two different cameras to calculate a depth of field and then tries to simulate the “bokeh” effect via software you would normally get in bulky professional cameras, by using fast optics at a wide aperture.
On the other side, when you hit the button on the Pixel phone, it is capturing a bunch of pictures and then decides what to keep from every picture in order to give the user the final result.
This challenges the concepts of originality and authenticity. The light captured by the camera is no longer the output of the photography process, but just the first step of a more complex process based on a multitude of factors. There is little doubt that this is just the beginning of a trend which will explode in the next few years. Continue reading
Customers often ask us about the hardware requirements for our products before purchasing. While we have some recommendation, the reality is that many of our customers use Amped FIVE (or our other products) on unbelievably old computers. Sure, Amped FIVE would be slower, but for working on low-resolution CCTV videos, even a 10-year-old PC with Windows XP (not recommended!) still works mostly fine.
I recently taught one of our courses at a customer’s premises. Since the class was quite full and they didn’t have enough recent laptops to bring into the training room, about half of the students had pretty old laptops. During the training, we normally provide the software installer and training examples on USB drives. Some people claimed that their PC was not able to see the drive. We figured out that they were using Windows XP SP2, they were not connected to the Internet and had not updated in ages. Continue reading
Our customers often ask us for specific functions and filters to be included in our software. Paying attention to user requests and managing our development priorities according to them is probably one of the things Amped Software is best known for.
However, not all requests, even if technically feasible, are a match for the purpose of our solutions. Take for example Amped FIVE: some of the most common use cases are enhancing license plates or faces.
Quite a few customers asked for some functions to perform super resolution of faces from a single image. While this may be technically very interesting, most of the implementations have a fatal flaw that prevents them from being used for forensic applications: they introduce data external to the case.
Yesterday a GitHub project called “srez” caught my attention.
Image super-resolution through deep learning. This project uses deep learning to upscale 16×16 images by a 4x factor. The resulting 64×64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Here’s an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16×16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
After the attacks of Paris, Brussels (and unfortunately many others), three days ago there was another major event at Istanbul Airport. While its origin is yet to be officially confirmed, strong hints are again at ISIS terror strategy. The number of victims is currently set at more than 40 and growing, with more than 160 persons injured. This is, again and again, a very sad story and our prayers are with the victims, the wounded and their family.
As usual, in these major events, it is interesting to analyze the different audio and video sources and their use. Continue reading