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Description

Threat detection entails a thorough examination of each individual network packet along with its contained data, featuring elements such as network protocol identification and verification, which allows for the identification of both obscure and concealed protocols. It incorporates machine learning techniques that provide a proactive assessment of traffic risk through scoring systems. Additionally, the detection of network steganography helps uncover hidden traffic within the network, including potential data breaches, espionage activities, and botnet communications. Utilizing proprietary algorithms for steganography detection serves as an efficient means of revealing various information concealment strategies. Furthermore, a unique signature database containing an extensive array of recognized network steganography techniques enhances detection capabilities. Forensic analysis is employed to effectively evaluate the ratio of security incidents relative to the traffic source. Facilitating the extraction of high-risk network traffic aids in concentrating analysis on specific threat levels, while storing processed traffic metadata in an extended format accelerates the trend analysis process. This multifaceted approach ensures a comprehensive understanding of network security challenges and enhances the ability to respond to emerging threats.

Description

An accessible platform that incorporates cutting-edge methods for detecting deepfakes is available for use. Users can upload a single video file from their computer at a time. Deepfakes, which are AI-generated fabricated media, can create misleading representations of individuals and their actions, posing significant risks when misused. The DeepFake-o-meter, created by the UB Media Forensics Lab, is an open-source tool designed to identify deepfake technologies created by third parties. It offers a user-friendly service that allows for the analysis of deepfake content using various advanced detection techniques, ensuring that results are delivered securely and privately. Additionally, it features an API framework that enables developers to integrate their deepfake detection algorithms and execute them on a remote server. Furthermore, it serves as a valuable resource for researchers in digital media forensics, providing a platform for assessing and benchmarking the effectiveness of different detection algorithms against one another. This multifaceted approach ultimately enhances the fight against the misuse of deepfake technology.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Cryptomage

Founded

2015

Country

Poland

Website

cryptomage.io/cybereye

Vendor Details

Company Name

UB Media Forensics Lab

Founded

2020

Country

United States

Website

zinc.cse.buffalo.edu/ubmdfl/deep-o-meter/

Product Features

Cybersecurity

AI / Machine Learning
Behavioral Analytics
Endpoint Management
IOC Verification
Incident Management
Tokenization
Vulnerability Scanning
Whitelisting / Blacklisting

Product Features

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