Model Overview

Description:

Hive's Deepfake Image Detection model identifies whether or not an image is a deepfake.

The model locates faces in an image. For each detected face, this model outputs a bounding box for its location, a classification, and accompanying confidence score.

Image classification model that classifies whether an image is a deepfake.

This model is ready for commercial and non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.

License/Terms of Use:

https://thehive.ai/terms-of-use

References (Leave Blank If None):

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)]

Network Architecture: EfficientNet-B4, YoloV8

Input:

Input Type(s): Ensemble Gradient Boosted (EGB) Image

Input Format(s): PNG, JPEG, or JPG

Input Parameters: Two-Dimensional (2D)

Output:

Output Type(s): Image with bounding boxes, Text

Output Format: JPEG, String

Output Parameters: Two-Dimensional (2D) Image with the same shape as input, One Dimensional (1D)

Software Integration:

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Volta

[Preferred/Supported] Operating System(s): Linux

Model Version(s):

v1.0

Training Dataset:

** Data Collection Method by dataset

Unknown

** Labeling Method by dataset

Unknown

Properties (Quantity, Dataset Descriptions, Sensor(s)): Trained on a large dataset comprising millions of artificially generated images and human-created images such as photographs, digital and traditional art, and memes sourced from across the web.

Inference:

Engine: TensorRT

Test Hardware: L40

Ethical Considerations (For NVIDIA Models Only):

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

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