加入随机透明底噪
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@ -2,7 +2,7 @@ import os
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import random
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import logging
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import json
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from PIL import Image
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from PIL import Image, ImageChops
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import traceback
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from typing import List, Tuple, Dict, Any, Optional
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import concurrent.futures
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@ -512,63 +512,6 @@ class PosterNotesCreator:
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logger.error(f"智能裁剪+重缩放时出错: {e}")
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return image # 出错时返回原图
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def optimize_anti_hash_methods(self, image: Image.Image, strength: str = "medium") -> Image.Image:
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"""优化后的哈希对抗方法，专注于裁剪缩放和DCT噪声"""
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logger.info(f"--- 开始优化抗哈希方法 (强度: {strength}) - 新策略 ---")
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original_image_for_logging = image.copy()
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# --- 参数定义 ---
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if strength == "low":
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phash_intensity = 0.06 # 轻微DCT噪声
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color_hist_strength = 0.02 # 轻微颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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elif strength == "high":
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phash_intensity = 0.20 # 较强DCT噪声
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color_hist_strength = 0.06 # 较强颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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else: # medium
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phash_intensity = 0.12 # 中等DCT噪声
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color_hist_strength = 0.04 # 中等颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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logger.debug(f"参数: pHash强度={phash_intensity:.2f}, 颜色强度={color_hist_strength:.2f}, 应用裁剪缩放={apply_crop_resize}")
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processed_image = image # 从原图开始
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# 1. 智能裁剪 + 重缩放 (策略C) - 作为第一步，改变像素基准
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if apply_crop_resize:
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processed_image = self.apply_smart_crop_resize(processed_image, strength)
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# 内部已有日志
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# 2. 强化的pHash对抗方法 (策略A)
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logger.debug(f"应用 pHash 对抗 (强化DCT噪声), 强度={phash_intensity:.2f}")
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processed_image = self.add_phash_noise(processed_image, intensity=phash_intensity)
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# 内部已有日志
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# 3. 保留轻微的颜色直方图扰动 (可选，影响相对小)
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if color_hist_strength > 0:
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logger.debug(f"应用颜色直方图扰动, 强度={color_hist_strength:.3f}")
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processed_image = self.perturb_color_histogram(processed_image, strength=color_hist_strength)
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# 内部已有日志
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# --- 移除了之前的 aHash块, dHash线, 区域变换, 高斯噪声等 ---
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logger.debug("移除了 aHash块, dHash线, 区域变换, 高斯噪声等局部微扰方法。")
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# 对比修改前后
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try:
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diff = ImageChops.difference(original_image_for_logging, processed_image).getbbox()
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if diff:
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logger.info(f"图像已修改。差异区域: {diff}")
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else:
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logger.warning("！！！优化方法似乎未修改图像！！！")
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except ValueError: # 如果图像模式不同(例如灰度vs彩色)，会引发ValueError
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logger.warning("无法比较图像差异：模式可能不同。")
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except Exception as log_e:
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logger.warning(f"无法比较图像差异: {log_e}")
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logger.info(f"--- 完成优化抗哈希方法 (强度: {strength}) - 新策略 ---")
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return processed_image
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def perturb_color_histogram(self, image: Image.Image, strength: float = 0.03) -> Image.Image:
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"""
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扰动图像的颜色直方图，对抗基于颜色统计的图像匹配
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@ -668,6 +611,121 @@ class PosterNotesCreator:
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logger.error(f"移除元数据时出错: {e}")
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return image # 出错时返回原图
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def apply_overlay_noise(self, image: Image.Image, alpha: int = 10, noise_type: str = 'uniform') -> Image.Image:
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"""
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在图像上叠加一个低透明度的噪声图层
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Args:
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image: 输入图像
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alpha: 叠加噪声图层的 Alpha 值 (0-255)
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noise_type: 'gaussian' 或 'uniform'
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Returns:
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叠加噪声后的图像
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"""
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try:
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logger.debug(f"应用低透明度噪声叠加: alpha={alpha}, type={noise_type}")
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# 确保图像是 RGBA 模式以处理透明度
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if image.mode != 'RGBA':
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base_image = image.convert('RGBA')
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else:
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base_image = image.copy() # 操作副本
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width, height = base_image.size
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# 创建噪声图层 (灰度噪声即可)
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if noise_type == 'gaussian':
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# 生成范围在 0-255 的高斯噪声，均值128
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noise_array = np.random.normal(loc=128, scale=40, size=(height, width)).clip(0, 255).astype(np.uint8)
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else: # uniform
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noise_array = np.random.randint(0, 256, size=(height, width), dtype=np.uint8)
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noise_image = Image.fromarray(noise_array, mode='L')
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# 将噪声灰度图转换为 RGBA，并设置 alpha 通道
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noise_rgba = noise_image.convert('RGBA')
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# 创建一个全为指定 alpha 值的通道
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alpha_channel = Image.new('L', noise_image.size, alpha)
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noise_rgba.putalpha(alpha_channel)
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# 使用 alpha_composite 进行混合叠加
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# alpha_composite 要求两个输入都是 RGBA
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combined_image = Image.alpha_composite(base_image, noise_rgba)
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# 通常我们希望最终结果是 RGB，所以转换回去
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# 如果原图就是 RGBA 且需要保留透明度，则省略此步
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final_image = combined_image.convert('RGB')
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logger.debug("低透明度噪声叠加应用成功。")
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return final_image
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except Exception as e:
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logger.error(f"应用叠加噪声时出错: {e}")
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logger.error(traceback.format_exc()) # 打印详细错误
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return image # 出错时返回原图
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def optimize_anti_hash_methods(self, image: Image.Image, strength: str = "medium") -> Image.Image:
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"""优化后的哈希对抗方法，策略: 裁剪缩放 + DCT噪声 + 叠加噪声"""
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logger.info(f"--- 开始优化抗哈希方法 (强度: {strength}) - 叠加噪声策略 ---")
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original_image_for_logging = image.copy()
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# --- 参数定义 ---
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if strength == "low":
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phash_intensity = 0.06 # 轻微DCT噪声
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color_hist_strength = 0.02 # 轻微颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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noise_alpha = random.randint(5, 8) # 低透明度噪声
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elif strength == "high":
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phash_intensity = 0.20 # 较强DCT噪声
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color_hist_strength = 0.06 # 较强颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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noise_alpha = random.randint(12, 18) # 较高透明度噪声
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else: # medium
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phash_intensity = 0.12 # 中等DCT噪声
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color_hist_strength = 0.04 # 中等颜色扰动
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apply_crop_resize = True # 应用裁剪缩放
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noise_alpha = random.randint(8, 12) # 中等透明度噪声
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logger.debug(f"参数: pHash强度={phash_intensity:.2f}, 颜色强度={color_hist_strength:.2f}, 应用裁剪缩放={apply_crop_resize}, 噪声Alpha={noise_alpha}")
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processed_image = image # 从原图开始
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# 1. 智能裁剪 + 重缩放 (策略C)
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if apply_crop_resize:
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processed_image = self.apply_smart_crop_resize(processed_image, strength)
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# 2. 强化的pHash对抗方法 (策略A)
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logger.debug(f"应用 pHash 对抗 (强化DCT噪声), 强度={phash_intensity:.2f}")
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processed_image = self.add_phash_noise(processed_image, intensity=phash_intensity)
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# 3. 保留轻微的颜色直方图扰动
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if color_hist_strength > 0:
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logger.debug(f"应用颜色直方图扰动, 强度={color_hist_strength:.3f}")
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processed_image = self.perturb_color_histogram(processed_image, strength=color_hist_strength)
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# 4. 应用低透明度噪声叠加 (新策略核心)
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if noise_alpha > 0:
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processed_image = self.apply_overlay_noise(processed_image, alpha=noise_alpha, noise_type='uniform') # 先用 uniform 试试
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# 内部已有日志
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# --- 移除之前的 aHash块, dHash线, 区域变换, 高斯噪声等 ---
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logger.debug("已移除 aHash/dHash特定对抗、区域变换、高斯噪声等。")
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# 对比修改前后
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try:
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diff = ImageChops.difference(original_image_for_logging.convert('RGB'), processed_image.convert('RGB')).getbbox() # 确保模式一致
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if diff:
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logger.info(f"图像已修改。差异区域: {diff}")
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else:
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logger.warning("！！！优化方法似乎未修改图像！！！")
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except ValueError:
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logger.warning("无法比较图像差异：模式可能不同或错误。")
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except Exception as log_e:
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logger.warning(f"无法比较图像差异: {log_e}")
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logger.info(f"--- 完成优化抗哈希方法 (强度: {strength}) - 叠加噪声策略 ---")
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return processed_image
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def optimized_process_image(
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self,
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image: Image.Image,
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