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LEAP is an automated tool for quick analysis of data for a variety of devices such as mobiles. It provides automated media classification to detect illicit content. It can analyse communication patterns, verify identities, countries of origin, and quickly screen persons of interest.

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Benefits of LEAP



LEAP has been designed to ingest the common mobile extraction formats: Cellebrite UFDR, MSAB Extended XML, Oxygen Forensics XML, Grayshift Graykey ZIP, MobilEdit XML and UFDR, .E01 forensic images and Raw Disk Images.


Easy to use

LEAP is designed to be simple to use and leverages a web-based GUI. LEAP allows access on multiple devices & platform types without the need to install a client on each one


Rapid access to key evidence

LEAP’s powerful media analysis engine is capable of rapidly screening thousands of images, videos and documents and uses machine learning technology to identify relevant content


Automated reporting

Automatic reporting + aggregated data allows investigators to make decisions faster. This helps to progress  investigations without waiting for manual analysis & reporting processes.


Best-in-class AI Classifiers

LEAP employs powerful deep learning architecture, utilitsing object recognition developed & trained by our team of experts. Automated analysis of pictures, videos & document files allows the detection of over 20 specific object classes. 


Cloud ready

LEAP is fully containerised, allowing for easy adoption into common cloud and edge environments. Due to the modular containerized setup, LEAP in the cloud can be scaled horizontally and vertically.

System Requirements

General Requirements:

  • GPU: The use of powerful GPUs is highly recommended when using image-object recognition features

  • OS: Ubuntu Linux 20.04 LTS (Ubuntu Linux 22.04 LTS will be supported soon)

  • Storage: LEAP & OS itself need approximately 400GB (including backups); the storage solution's size should be aligned to the amount of data to be processed and their storage times. LEAP’s performance benefits immensely from using NVMe SSDs for storing extractions

The following requirements are meant as reference point for potential system environments:

Small solution

These are the minimum requirements for running between 5 to 10 mid-size extractions a day using image-object recognition:

  • CPU: At least 10th Generation Intel Core i7, AMD Ryzen 7 3000-series or comparable

  • RAM: 128GB

  • GPU: NVIDIA Quadro RTX 4000 or better CUDA-enabled card

Mid to large size solution
For high throughput and time-critical environments, T3K recommends the following minimum requirements:

  • CPU: 2x Intel Xeon Gold 6142/1x AMD Ryzen Threadripper 2990WX or better CPUs with at least 32 cores/64 threads (2.8GHz base/3.7GHz turbo per core)

  • RAM: 192GB DDR4

  • GPU: NVIDIA RTX A6000 or better CUDA-enabled card


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