AI Face Swap on Video Implementation

Development of an AI system for replacing faces in videos (Face Swap)

Face swap is a technology for replacing faces in videos. It is used in entertainment applications, visualization of clothing/accessories, educational materials with anonymization, and production (stuntman replacement). It requires strict compliance with the law and obtaining consent.

Roop / Reactor — open source solutions

import subprocess
import os

class FaceSwapService:
    def __init__(self, model_dir: str = "./models"):
        self.model_dir = model_dir
        # Используем InsightFace для детекции + sim swap для замены

    def swap_face_in_video(
        self,
        source_face: str,    # Изображение с исходным лицом
        target_video: str,   # Видео, в котором меняем лицо
        output_path: str,
        face_index: int = 0  # Какое лицо в target заменяем
    ) -> str:
        subprocess.run([
            "python", "roop/run.py",
            "--source", source_face,
            "--target", target_video,
            "--output", output_path,
            "--face-restore",
            "--frame-processor", "face_swapper", "face_enhancer",
            "-e", "colab-run"
        ], check=True)
        return output_path

    def batch_swap(self, face_path: str, video_paths: list[str], output_dir: str) -> list[str]:
        results = []
        for video in video_paths:
            filename = os.path.basename(video)
            output = os.path.join(output_dir, f"swapped_{filename}")
            results.append(self.swap_face_in_video(face_path, video, output))
        return results

InsightFace Direct (Advanced Control)

import insightface
import cv2
import numpy as np
from gfpgan import GFPGANer

class AdvancedFaceSwapper:
    def __init__(self):
        self.face_analysis = insightface.app.FaceAnalysis(
            name="buffalo_l",
            providers=["CUDAExecutionProvider"]
        )
        self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
        self.swapper = insightface.model_zoo.get_model(
            "inswapper_128.onnx",
            download=True
        )
        self.enhancer = GFPGANer(model_path="GFPGANv1.4.pth", upscale=1)

    def swap_faces_in_frame(
        self,
        source_face_embedding,
        frame: np.ndarray,
        target_face_idx: int = 0
    ) -> np.ndarray:
        faces = self.face_analysis.get(frame)
        if not faces or target_face_idx >= len(faces):
            return frame

        target_face = faces[target_face_idx]
        result = self.swapper.get(frame, target_face, source_face_embedding, paste_back=True)

        # Улучшаем лицо после замены
        _, _, result = self.enhancer.enhance(result, has_aligned=False, paste_back=True)
        return result

    def process_video(
        self,
        source_image: str,
        target_video: str,
        output_path: str
    ) -> str:
        # Получаем embedding исходного лица
        source_img = cv2.imread(source_image)
        source_faces = self.face_analysis.get(source_img)
        if not source_faces:
            raise ValueError("Лицо не найдено в source image")
        source_embedding = source_faces[0]

        cap = cv2.VideoCapture(target_video)
        fps = cap.get(cv2.CAP_PROP_FPS)
        w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

        while True:
            ret, frame = cap.read()
            if not ret:
                break
            processed = self.swap_faces_in_frame(source_embedding, frame)
            out.write(processed)

        cap.release()
        out.release()
        return output_path

Legal Restrictions and Ethics

Forbidden:

• Deepfake pornography (criminal in many jurisdictions)
• Creating fake content featuring real people without their consent
• Use in disinformation, fraud

Required:

• Written consent from the person whose face is being used
• Explicitly marking content as AI-generated
• Policy for the storage and deletion of biometric data (Federal Law No. 152)

Adding a watermark:

def add_ai_watermark(video_path: str, output_path: str) -> str:
    subprocess.run([
        "ffmpeg", "-i", video_path,
        "-vf", "drawtext=text='AI GENERATED':fontsize=24:fontcolor=white:alpha=0.7:x=10:y=10",
        output_path
    ], check=True)
    return output_path

Timeframe: Face Swap service for images – 1 week. For videos with queue and face enhancement – 2–3 weeks.