TABLE OF CONTENTS

AI Server: TensorFlow Integration for Image/Video Recognition

AccessData AI server is built on top of Google’s TensorFlow technology. It provides the capability to run image/video recognition with the use of pretrained models packaged within the application. These models can be utilised against multiple objects and cases and can be fully utilized with FTK Plus.


Image recognition is a resource intensive process. However, the AI Server supports Nvidia CUDA GPU acceleration. CUDA support for your GPU can be checked here.

Technology Employed by FTK

AI Server – TensorFlow             

Google’s TensorFlow uses various algorithms and models to help in image/video recognition and classification tasks. The AI server is built on top of this technology which provides you the capability to train on custom objects/faces and then identify similar objects/faces with an AI model.


Supported TensorFlow AI Jobs

AI Job Type

Description

Image Recognition

Utilises pre-created AI models for object-based image recognition during evidence processing.

Live Facial Recognition

Utilises pre-created AI models for facial-based image recognition during evidence processing.

Video Recognition

Utilises pre-created AI models for object-based video recognition during evidence processing.

 

Installation

The following steps must be followed on the machine where the GPU intended for AI jobs is located.


Prerequisites

  • AI Server
    • Python 3.10 x64 (Installed with AI Server)
  • CUDA 10 or CUDA 11.2 (Optional)
  • cuDNN compatible with CUDA-enabled GPU – A developer account will be needed, this can be created for free on the NVidia website.

CUDA 10/11 Installation (Optional for GPU use)

  1. Run the CUDA exe as an administrator.
  2. Select an extraction path and click OK.
  3. Review the License Agreement and click AGREE AND CONTINUE.
  4. Select Custom (Advanced) and click NEXT.
  5. Expand CUDA and deselect Visual Studio Integration and click NEXT.
  6. Ensure the installation locations are noted and click NEXT.
  7. Click CLOSE once the installation is complete.

cuDNN 7.4 Installation for CUDA 10 (Optional for GPU use)

  1. Locate and open the directory for NVIDIA GPU Computing Toolkit.
  2. Navigate to the CUDNN archive and open it.
  3. Copy and paste the following files:
    1. Open \cuda\bin\ and copy cudnn64_7.dll to where \NVIDIA GPU Computing Toolkit\CUDA\v10.*\bin\ is located.
    2. Open \cuda\include\ and copy cudnn.h to where \NVIDIA GPU Computing Toolkit\CUDA\v10.*\include\ is located.
    3. Open \cuda\lib\x64\ and copy cudnn.lib to where \NVIDIA GPU Computing Toolkit\CUDA\v10.*\lib\x64 is located.


cuDNN 8.1 Installation for CUDA 11.2 (Optional)

  1. Locate and open the directory for NVIDIA GPU Computing Toolkit.
  2. Navigate to the CUDNN archive and open it.
  3. Copy and paste the following files:
    1. Open \cuda\bin\ and copy all the available DLL files to where \NVIDIA GPU Computing Toolkit\CUDA\v11.2*\bin\ is located.
    2. Open \cuda\include\ and copy cudnn.h to where \NVIDIA GPU Computing Toolkit\CUDA\v11.2*\include\ is located.
    3. Open \cuda\lib\x64\ and copy cudnn.lib to where \NVIDIA GPU Computing Toolkit\CUDA\v11.2*\lib\x64 is located.


AI Server Installation

  1. Run AccessData_AI_Server_x64.exe as an administrator.
  2. Click Install if prompted to install Python 3.10.
  3. Click Next on the Welcome screen.
  4. Review and Accept the License Agreement, then click Next.
  5. Check Install for GPU use if utilising a CUDA enabled graphics card (Optional).
  6. At the User Credentials screen, enter the credentials for an account to run the AI service and click Next.
    • This account should be a member of the local administrators group, and be a domain-level account in a multi-box environment. The "Local System" account should only be used if all components, as well as case and evidence storage, will be on one single machine. 
  7. Click Install.
  8. Click Finish.

Configuring the AI Server for FTK & FTK Plus

Either configuration method can be followed.

Manually Editing the Configuration File

  1. Navigate to \Program Files\AccessData\Forensic Tools\<version>\bin\.
  2. Open ADG.WeblabSelfHost.exe.config in a text editor.
  3. Update the value for the TensorFlow URL and save the changes.
    • Any changes made in this file must be then appended by restarting the Exterro self-host service.


Adding the TensorFlow URL via the FTK UI

  1. Login to the FTK application.
  2. Click Tools > Preferences.
  3. Click Configure AccessData Servers.
  4. Enter the AI Server URL.
  5. Click Save.