隱形盾牌:AI 影片浮水印如何解決 2025 年的版權危機

# 隱形浮水印管線的簡化概念
class InvisibleWatermarker:
    def __init__(self, key: bytes):
        self.encoder = FrequencyDomainEncoder(key)
        self.decoder = RobustDecoder(key)
 
    def embed(self, video_frames: np.ndarray, payload: bytes) -> np.ndarray:
        # 轉換到頻域(DCT/DWT)
        freq_domain = self.to_frequency(video_frames)
 
        # 在中頻係數中嵌入有效載荷
        # 低頻 = 可見變化
        # 高頻 = 被壓縮破壞
        # 中頻 = 最佳位置
        watermarked_freq = self.encoder.embed(freq_domain, payload)
 
        return self.to_spatial(watermarked_freq)
 
    def extract(self, video_frames: np.ndarray) -> bytes:
        freq_domain = self.to_frequency(video_frames)
        return self.decoder.extract(freq_domain)

# 概念性 SynthID 整合
class WatermarkedVideoGenerator:
    def __init__(self, base_model, synthid_encoder):
        self.model = base_model
        self.synthid = synthid_encoder
 
    def generate(self, prompt: str, watermark_id: str) -> Video:
        # 在潛在空間中生成
        latent_video = self.model.generate_latent(prompt)
 
        # 在解碼前嵌入浮水印
        watermarked_latent = self.synthid.embed(
            latent_video,
            payload=watermark_id
        )
 
        # 解碼到像素空間
        return self.model.decode(watermarked_latent)

# 優化景觀
def watermark_quality_score(
    latency_ms: float,
    bit_error_rate: float,
    psnr_db: float,
    compression_survival: float
) -> float:
    # 實際系統使用加權組合
    # 這些權重取決於使用案例
    return (
        0.2 * (1 / latency_ms) +      # 延遲越低 = 越好
        0.3 * (1 - bit_error_rate) +   # BER 越低 = 越好
        0.2 * (psnr_db / 50) +         # PSNR 越高 = 品質越好
        0.3 * compression_survival      # 倖存率越高 = 越好
    )

# 概念性 GaussianSeal 方法
class GaussianSealWatermark:
    def embed_in_gaussians(
        self,
        gaussians: List[Gaussian3D],
        payload: bytes
    ) -> List[Gaussian3D]:
        # 修改 Gaussian 參數(位置、協方差、不透明度)
        # 以下列方式:
        # 1. 從所有視點保持視覺品質
        # 2. 編碼可恢復的位元模式
        # 3. 在常見 3D 操作中倖存
 
        for i, g in enumerate(gaussians):
            bit = self.get_payload_bit(payload, i)
            g.opacity = self.encode_bit(g.opacity, bit)
 
        return gaussians

# 範例:強健的時間嵌入
def embed_with_redundancy(
    frames: List[np.ndarray],
    payload: bytes,
    redundancy_factor: int = 5
) -> List[np.ndarray]:
    watermarked = []
    for i, frame in enumerate(frames):
        # 每 N 個影格嵌入相同的有效載荷
        if i % redundancy_factor == 0:
            frame = embed_payload(frame, payload)
        watermarked.append(frame)
    return watermarked

class WatermarkDetector:
    def __init__(self, model_path: str):
        self.model = load_detection_model(model_path)
 
    def detect(self, video_path: str) -> DetectionResult:
        frames = extract_key_frames(video_path, n=10)
 
        results = []
        for frame in frames:
            payload = self.model.extract(frame)
            confidence = self.model.confidence(frame)
            results.append((payload, confidence))
 
        # 跨影格的多數投票
        return self.aggregate_results(results)