# 克隆仓库
git clone https://github.com/aloshdenny/reverse-SynthID.git
cd reverse-SynthID

# 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# 安装依赖
pip install -r requirements.txt

# 下载所有参考图像
pip install huggingface_hub
python scripts/download_images.py

# 或下载特定文件夹
python scripts/download_images.py gemini_black  # 下载黑图参考
python scripts/download_images.py gemini_white  # 下载白图参考

from src.extraction.synthid_bypass import SpectralCodebook

# 创建码本
codebook = SpectralCodebook()

# 配置文件1：从黑/白参考图像（1024x1024）
codebook.extract_from_references(
    black_dir='gemini_black',
    white_dir='gemini_white',
)

# 配置文件2：从带水印内容图像（1536x2816）
codebook.build_from_watermarked('gemini_random')

# 保存码本
codebook.save('artifacts/spectral_codebook_v3.npz')

from src.extraction.synthid_bypass import SpectralCodebook, SynthIDBypass

# 加载码本
codebook = SpectralCodebook()
codebook.load('artifacts/spectral_codebook_v3.npz')

# 执行V3绕过
bypass = SynthIDBypass()
result = bypass.bypass_v3(image_rgb, codebook, strength='aggressive')

print(f"PSNR: {result.psnr:.1f} dB")
print(f"使用配置: {result.details['profile_resolution']}")
print(f"精确匹配: {result.details['exact_match']}")

# 构建码本
python src/extraction/synthid_bypass.py build-codebook \
    --black gemini_black \
    --white gemini_white \
    --watermarked gemini_random \
    --output artifacts/spectral_codebook_v3.npz

# 执行V3绕过
python src/extraction/synthid_bypass.py bypass \
    input.png output.png \
    --codebook artifacts/spectral_codebook_v3.npz \
    --strength aggressive

from src.extraction.robust_extractor import RobustSynthIDExtractor

# 初始化检测器
extractor = RobustSynthIDExtractor()
extractor.load_codebook('artifacts/codebook/robust_codebook.pkl')

# 检测图像
result = extractor.detect_array(image)

print(f"带水印: {result.is_watermarked}")
print(f"置信度: {result.confidence:.4f}")

python src/extraction/robust_extractor.py detect image.png \
    --codebook artifacts/codebook/robust_codebook.pkl

# 置信度 = 相位一致性 × 交叉验证一致性
confidence = phase_consistency × cross_validation_agreement

# DC排除 — 软斜率抑制低频生成偏差
# 每bin相减 = wm_magnitude × confidence × removal_fraction × channel_weight
# 安全上限 — 相减永不超过任何bin处图像能量的90-95%
# 多通道 — 递减强度调度（激进 → 中等 → 温和）捕获残余能量

from src.extraction.synthid_bypass import SpectralCodebook, SynthIDBypass

# 频谱码本——多分辨率水印指纹
codebook = SpectralCodebook()

# 从黑/白参考图像添加 1024x1024 配置文件
codebook.extract_from_references('gemini_black', 'gemini_white')

# 从带水印图像添加 1536x2816 配置文件
codebook.build_from_watermarked('gemini_random')

# 保存和加载
codebook.save('codebook.npz')
codebook.load('codebook.npz')

# 自动选择匹配分辨率
profile, res, exact = codebook.get_profile(1536, 2816)

# SynthIDBypass——三代绕过
bypass = SynthIDBypass()

# V1: JPEG压缩
result = bypass.bypass_simple(image, jpeg_quality=50)

# V2: 多级变换
result = bypass.bypass_v2(image, strength='aggressive')

# V3: 多分辨率频谱码本相减（最佳）
result = bypass.bypass_v3(image, codebook, strength='aggressive')

from src.extraction.robust_extractor import RobustSynthIDExtractor

# 多尺度水印检测器（90%准确率）
extractor = RobustSynthIDExtractor()
extractor.load_codebook('artifacts/codebook/robust_codebook.pkl')

# 检测图像数组
result = extractor.detect_array(image)
print(f"带水印: {result.is_watermarked}, 置信度: {result.confidence:.4f}")

# 下载所有参考图像
python scripts/download_images.py

# 下载特定文件夹
python scripts/download_images.py gemini_black
python scripts/download_images.py gemini_white