Entertainment

Deepfake Detection

Identify and flag manipulated media by analyzing facial anomalies, audio-visual synchronization, and physiological cues.

12.5K runs

Deploy Recipe

from mixpeek import Mixpeek

client = Mixpeek("your-api-key")

# Create a collection for deepfake detection
client.collections.create(
    feature_extractors=[
        "faceforensics-xception",
        "blink-frequency",
        "rppg-signal",
        "av-syncnet"
    ]
)

Feature Extractors

XceptionNet Deepfake Detector

Detects manipulated facial regions using a CNN trained on the FaceForensics++ dataset.

340K runs

Blink Frequency Estimator

Estimates eye blink frequency to detect unnatural blinking behavior common in GAN-generated faces.

150K runs

Remote Photoplethysmography (rPPG)

Extracts pulse signals from facial skin tone changes to detect missing physiological cues in synthetic faces.

72K runs

Audio-Visual SyncNet

Detects misalignment between lip movements and audio to catch audio-driven deepfakes.

204K runs

Retrievers

KNN Search

K-Nearest Neighbor search for finding similar vectors based on distance metrics

450K runs

Industry Resources

Entertainment Resources