القائمة
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
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استنادا إلى تصنيف SOC المهني
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| name | cv-centroid-distance-anomaly-score |
| description | Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function |
In a real video, all face embeddings should cluster tightly (same person, consistent appearance). Deepfakes introduce subtle inconsistencies that push some embeddings away from the centroid. Computing the L2 distance from each frame's face embedding to the mean embedding, then averaging and passing through a logistic function, produces a calibrated deepfake probability without needing labeled training data.
import numpy as np
def centroid_anomaly_score(embeddings, bias=-0.3, weight=0.7):
centroid = embeddings.mean(axis=0)
distances = np.linalg.norm(embeddings - centroid, axis=1)
mean_dist = distances.mean()
prob = 1.0 / (1.0 + np.exp(-(bias + weight * mean_dist)))
return prob
# embeddings: (N_frames, 512) from a pretrained face model
prob_fake = centroid_anomaly_score(face_embeddings)