菜单
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
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Extract per-frame CNN features then classify the temporal sequence with stacked GRU layers and a boolean mask for variable-length video inputs
| 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)