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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
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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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)