Category Archives: New features

Amped Authenticate Update 11362: JPEG Dimples, Improved JPEG HT, Social Media Identification, and much more!

Not long has passed since the release of Amped Authenticate 10641 but… yes, the next one is already out! Amped Authenticate 11362 is now released with a lot of improvements, including two new filters based on JPEG Dimples, one of the last discoveries of the image forensics scientific community!

JPEG Dimples

Despite many attempts to send JPEG into retirement, today the vast majority of digital images still use it. Amped Authenticate users know that traces left by JPEG compression are a superb asset when it comes to investigating the digital history of an image, as witnessed by the vast JPEG-based toolkit that Authenticate provides: quantization table analysis, JPEG ghosts, inconsistencies in blocking artifacts, double quantization traces in the DCT coefficients, and more.

But JPEG is still full of new surprises nowadays! A few months ago, while Amped was attending (and sponsoring!) the IEEE 2017 International Workshop on Information Forensics and Security (WIFS 2017), a new footprint was presented to the scientific community: JPEG Dimples (click here to see the original work Photo forensics from JPEG dimples by Shruti Agarwal and Prof. Hany Farid).

JPEG Dimples manifest themselves as a grid of slightly brighter/darker pixels, spaced by 8 pixels in each dimension. Like most image forensic fingerprints, even JPEG Dimples are hardly visible by the human eye, but they can be easily detected with a proper algorithm.

But why does this grid appear? And why is it important for our analysis? We’ll answer these questions in detail in a future blog post, however the reason behind JPEG Dimples is rather simple: during the DCT coefficients quantization phase, different operators exist to approximate decimal values to integer values: the round operator (which approximates the decimal number to the nearest integer) the floor operator (approximation to the nearest smaller integer) or the ceil operator (approximation to the nearest bigger integer). The table below shows the difference in approximating a Value (first column) to an integer using round, floor and ceil.

Value Round Floor Ceil
9.8 10 9 10
6.3 6 6 7
4.5 5 4 5
-7.3 -7 -8 -7

Obviously, using floor tends to produce smaller values in the 8-by-8 DCT block than using round, and the opposite with ceil. And when we go back to the pixel domain, this leads to a slightly darker or brighter pixel on the top-left corner of the pixel block (see example below)! Measuring the presence of this grid will tell us to which degree an image contains the JPEG Dimples footprint.

Image showing Dimples

Example of an image showing strong JPEG Dimples

Now you may be wondering “well, how many cameras will ever be using floor or ceil in place of the more classical round?” Not so few, actually. According to the work presented at WIFS 2017, more than 60% of tested cameras do introduce Dimples. We also carried out an internal evaluation on Amped datasets and numbers were less upsetting, still, we found Dimples in roughly 30% of tested cameras. A footprint with such a spread could not be missing in Amped Authenticate, and so here we are. Continue reading

Amped FIVE Update 11284: Multiplexed Stream Support, Proprietary Timestamp, Remove Frames Filter, and a Whole Lot More

Whilst it’s been a busy time for us here at Amped with the demand for training higher than ever, we have made sure our development is continuous and we’re here again with another huge update for Amped FIVE.

A Completely Revamped Conversion Engine

As you will know, one of the biggest struggles within the world of CCTV and video analysis is the ever-increasing number of proprietary formats. Our support and development team are constantly receiving requests for new format support and in our latest update, we have enabled conversion support for BVR, DVS, H64, PSF and SHV formats, along with some variations of other formats already supported in previous versions.

All these formats are multiplexed streams. This is when a manufacturer has placed all camera footage into a single time-based video stream.

The latest FIVE not only converts the files straight away, but demultiplexes each video stream, splitting them into their own individual chains within the software. Under the Convert DVR Advanced tab you will find the options to enable this time-saving function.

Files to Convert > All, one chain per file.

No more mixed streams, no more time wasted writing carving scripts. A few clicks will now save you hours!

Multiplexed single stream decoding is huge, so expect a dedicated blog post in the next few weeks looking more deeply into decoding files of this type.

But the new conversion engine does not stop there! There are a lot of benefits even on single stream video files. Standard conversion done with vanilla FFmpeg is often not enough – there may be the risk of losing video frames because of wrongly interpreted proprietary metadata. Our new engine not only cleans almost every proprietary video format, being in MPEG4, H263, H264 and H265, but for many of them also recovers the proprietary timestamp. We found more than 50 different variations of timestamp formats!

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Identify Social Media Files with Amped Authenticate

Amped Authenticate Update 10641 introduced the new Social Media Identification filter. It can be found in the File Analysis filter group.

The filters in the File Analysis group are generally looking at the file’s container to return relevant information about the file. The Social Media Identification filter examines the file for traces of information that may indicate the file’s social media source. The key word here is “may.”

The workflow that I will explain here is typical in the US and Canada. Take from it what you need in order to apply it to your country’s legal system.

Let’s begin.

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Amped Authenticate Update 10641: Social Media Identification, Griffeye Integration & Many New Filter Options

Hi everyone!

David Spreadborough here, the International Trainer at Amped Software. It’s great to be back writing a blog post. The past few months have been very busy at Amped and our image authentication software, Amped Authenticate, has become the ‘go-to’ tool for many requiring an image’s history or to identify signs of manipulation. Helped maybe by the huge amounts of press detailing fake stories and images.

In this crazy world of multimedia forensics, we cannot stand still. The tech wizards at Amped HQ have been hard at work integrating new filters and tools to assist you further. So, let’s dive in and take a look!

Social Media Identification Filter

Under the File Analysis category, you will find this new filter.

Its purpose is to detect traces in the file formats left on images by social media platforms. As most of you probably already know, it is very easy to save other people’s images from sharing sites. With a simple right-click, you can save the displayed image to your computer.

An image from someone’s Facebook timeline

This filter now enables you to identify if the images you are examining originate from a Social Media Platform.

Continue reading

Amped DVRConv for transcription?

In our most recent update of Amped DVRConv, we added the ability to separate the audio and video streams in your DME files – to save the audio as a separate file. For some, this functionality went unnoticed. For others, it was a huge deal.

Two very specific use cases required this functionality. You asked. We delivered.

Case #1 – Child Exploitation/Human Trafficking

Agencies responsible for investigating cases of child exploitation/human trafficking were spending a lot of time redacting video files (blurring faces and other sensitive information) in order to send files off for audio transcription. The distribution of files in child exploitation cases (files that can be considered child pornography) for transcription is now made a lot easier with DVRConv. All of the evidentiary videos can be loaded into the tool and processed without having to view the footage. DVRConv helps to dramatically speed up the process of getting files to transcription whilst protecting identities and shielding staff from the harmful psychological and legal effects of viewing/distributing such material.

Case #2 – Police Generated Video

Agencies that have deployed body worn/vehicle-based cameras or have interview room recorders often have to send the resulting video files to outside companies for transcription. Like the case above, they are faced with having to redact the visual information prior to releasing the files to their contractor. Even if the agency has chosen a CJIS compliant transcription contractor, they may have agency policies that require the redaction of the visual information prior to release. DVRConv eliminates the need to perform a visual redaction ahead of such a release of files. Having this ability is already saving agencies a tremendous amount of time/money.

Users of DVRConv do not require specialized training. The tool can be used by anyone. It’s drag-drop easy. Plus, the settings can be configured so that the resulting audio file meets the requirements of your transcription vendor.

If you’d like to know more about Amped DVRConv, or any of our other Amped Software products and training options, contact us today.

Amped DVRConv Update 10098: more formats, more speed, more options

Today we released an update to Amped DVRConv, the easiest way to convert videos from proprietary DVR formats.

We have been working on this update for some time and a few users have received beta updates in order to support formats that were urgently required. During this period we have re-engineered a good part of the architecture to improve stability, speed and format compatibility.

Continue reading

The Amped FIVE Assistant Video Tutorial

We recently announced the release of the latest version of Amped FIVE (10039) where we introduced a new operational mode through a panel called the “Assistant”.

The Assistant provides a set of predefined workflows which can be used to automate common operations or guide new users, but it’s not obtrusive. You can use it or not, and you can always add filters or do anything, as usual, it’s just an additional option.

We’ve created a video tutorial so you can see it in action. See below or watch on YouTube now!

We’ll be adding more videos to our YouTube channel soon, so follow us to get more videos like this.

The Sparse Selector

With over 100 filters and tools in Amped FIVE, it’s easy to lose track of which filter does what. A lot of folks pass right by the Sparse Selector, not knowing what it does or how to use it. The simple explanation of the Sparse Selector’s function is that it is a list of frames that are defined by the user. Another way of explaining its use: the Sparse Selector tool outputs multiple frames taken from random user selected positions of an input video.

How would that be helpful, you ask? Oh, it’s plenty helpful. Let me just say, it’s one of my favorite tools in FIVE. Here’s why.

#1. – Setting up a Frame Average

You want to resolve a license plate. You’ve identified 6 frames of interest where the location within the frame has original information that you’re going to frame average to attempt to accomplish your goal. Unfortunately, the frames are not sequentially located within the file. How do you select (easily / fast) only frames 125, 176, 222, 278 314, and 355? The Sparse Selector, that’s how.

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Amped FIVE Update 10039: Introducing the Assistant, and much more

I am not exaggerating when I say that this is one of the most important Amped FIVE updates in years!

In this version, we have released a new operational mode in Amped FIVE through a panel called the “Assistant”. The concept is extremely simple but opens a world of possibilities. I had this idea during a meeting in the US about one year ago. I hacked a quick and dirty prototype in a couple of hours and showed it to a few users at the LEVA conference to gather feedback. From then on, we worked to improve it and prepare some scripts.

In the last few years, Amped FIVE has grown like crazy, with more than 100 filters for every kind of issue. It has been adopted by experienced analysts and beginners, and used on cases of local, national and international level.

This raised a few interesting challenges:

  • With so many filters, how are you supposed to know the best tool to use in every case?
  • How do you enforce your agency SOPs for specific needs and workflows?
  • How do you easily help beginners and your new colleagues that are new to the job with the wealth of options available?
  • How do you automate repetitive and boring tasks?
  • How do you avoid human error with repeatable and documented practices?

We had a few ideas for a funny and helpful character.

Or maybe a more awesome classical wizard with its annoying modal interface.

Not really. The solution is much simpler.

Meet the Amped FIVE Assistant.

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Experimental validation of Amped Authenticate’s Camera Identification filter

We tested the latest implementation (Build 8782) of PRNU-based Camera Identification and Tampering Localization on a “base dataset” of 10.069 images, coming from 29 devices (listed in the table below). We split the dataset in two:
– Reference set: 1450 images (50 per device) were used for CRP estimation
– Test set: 8619 images were used for testing. On average, each device was tested against approximately 150 matching images and approximately 150 non-matching images.

It is important to understand that, in most cases, we could not control the image creation process. This means that images may have been captured using digital zoom or at resolutions different than the default one, which makes PRNU analysis ineffective. Making use of EXIF metadata, we could filter out these images from the Reference set. However, we chose not to filter out such images from the Test set: we prefer showing results that are closer to real-world cases, rather than tricking the dataset to obtain 100% performance.

Using the above base dataset, we carried out several experiments:
– Experiment 1) testing the system on images “as they are”
– Experiment 2) camera identification in presence or rotation, resize and JPEG re-compression
– Experiment 3) camera identification in presence of cropping, rotation and JPEG re-compression
– Experiment 4) discriminating devices of the same model
– Experiment 5) investigating the impact of the number of images used for CRP computation.

Continue reading