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Description
Developing convincingly realistic fake networks that can effectively impede, mislead, or manipulate an opponent is a demanding process that requires significant time, financial investment, and resources. Penten’s Deception.ai stands out as a formidable artificial intelligence solution that streamlines the creation and implementation of these intricate fake networks necessary for identifying, tracking, and countering advanced cyber threats. The platform's smart workflow offers guidance on designing your deceptive network and attack trajectory, planning scenarios, deploying them, and generating realistic, customized users along with content. These fabricated users engage with the environment, executing system and user tasks in a manner that closely mimics real human behavior, including activities such as reading and sending emails, editing files, making calls, and chatting with other users. This efficient approach ensures the rapid establishment of a lifelike environment that is critical for engaging adversaries effectively. Ultimately, leveraging such technology not only enhances security measures but also provides strategic advantages in cyber defense operations.
Description
We create a three-dimensional signed distance field (SDF) and a textured field using two latent codes. DMTet is employed to derive a 3D surface mesh from the SDF, and we sample the texture field at the surface points to obtain color information. Our training incorporates adversarial losses focused on 2D images, specifically utilizing a rasterization-based differentiable renderer to produce both RGB images and silhouettes. To distinguish between genuine and generated inputs, we implement two separate 2D discriminators—one for RGB images and another for silhouettes. The entire framework is designed to be trainable in an end-to-end manner. As various sectors increasingly transition towards the development of expansive 3D virtual environments, the demand for scalable tools that can generate substantial quantities of high-quality and diverse 3D content has become apparent. Our research endeavors to create effective 3D generative models capable of producing textured meshes that can be seamlessly integrated into 3D rendering engines, thereby facilitating their immediate application in various downstream uses. This approach not only addresses the scalability challenge but also enhances the potential for innovative applications in virtual reality and gaming.
API Access
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API Access
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Integrations
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Pricing Details
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Free Trial
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Free Version
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Pricing Details
No price information available.
Free Trial
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Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Penten
Founded
2014
Country
Australia
Website
www.penten.com/applied-ai/deception-ai/
Vendor Details
Company Name
NVIDIA
Country
United States
Website
nv-tlabs.github.io/GET3D/