优化了抗哈希检测的方法
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@ -475,6 +475,127 @@ class PosterNotesCreator:
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image.save(data, format=image.format if image.format else 'PNG')
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image.save(data, format=image.format if image.format else 'PNG')
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return Image.open(data)
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return Image.open(data)
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def optimize_anti_hash_methods(self, image: Image.Image, strength: str = "medium") -> Image.Image:
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"""
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综合优化的哈希对抗方法，针对性处理aHash、pHash和dHash
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Args:
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image: 输入图像
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strength: 处理强度 - "low", "medium", "high"
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Returns:
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处理后的图像
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"""
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# 根据强度设置参数
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if strength == "low":
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ahash_intensity = 0.03
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phash_intensity = 0.04
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dhash_intensity = 0.03
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elif strength == "high":
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ahash_intensity = 0.09
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phash_intensity = 0.08
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dhash_intensity = 0.09
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else: # medium
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ahash_intensity = 0.06
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phash_intensity = 0.06
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dhash_intensity = 0.06
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# 1. 针对aHash (平均哈希)的处理 - 专注于整体亮度变化
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# aHash对整体亮度敏感，创建局部亮度变化可有效对抗
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img_array = np.array(image)
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h, w = img_array.shape[0], img_array.shape[1]
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# 创建10-20个随机块，并调整其亮度
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num_blocks = random.randint(10, 20)
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for _ in range(num_blocks):
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# 随机选择块的位置和大小
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block_w = random.randint(w//20, w//10)
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block_h = random.randint(h//20, h//10)
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x = random.randint(0, w - block_w)
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y = random.randint(0, h - block_h)
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# 随机调整块的亮度
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delta = int(random.uniform(-25, 25) * ahash_intensity)
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if len(img_array.shape) == 3: # 彩色图像
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img_array[y:y+block_h, x:x+block_w, :] = np.clip(
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img_array[y:y+block_h, x:x+block_w, :] + delta, 0, 255)
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else: # 灰度图像
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img_array[y:y+block_h, x:x+block_w] = np.clip(
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img_array[y:y+block_h, x:x+block_w] + delta, 0, 255)
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# 转回PIL图像
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image = Image.fromarray(img_array.astype(np.uint8))
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# 2. 调用现有的pHash对抗方法
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image = self.add_phash_noise(image, intensity=phash_intensity)
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# 3. 针对dHash (差值哈希)的处理 - 添加细微梯度扰动
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# dHash对相邻像素梯度敏感，添加小梯度干扰非常有效
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img_array = np.array(image)
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# 计算图像尺寸并创建掩码
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mask = np.zeros_like(img_array, dtype=bool)
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grid_size = 8 # dHash通常是8x8
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# 在图像中选择8-12条边缘线进行干扰
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num_lines = random.randint(8, 12)
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for _ in range(num_lines):
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# 随机决定是水平线还是垂直线
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if random.random() < 0.5: # 水平线
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y = random.randint(0, h - 1)
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line_width = random.randint(1, 3)
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if len(mask.shape) == 3: # 彩色图像
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mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :, :] = True
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else: # 灰度图像
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mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :] = True
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else: # 垂直线
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x = random.randint(0, w - 1)
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line_width = random.randint(1, 3)
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if len(mask.shape) == 3: # 彩色图像
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mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1), :] = True
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else: # 灰度图像
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mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1)] = True
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# 应用梯度干扰
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if len(img_array.shape) == 3: # 彩色图像
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delta = (np.random.random(img_array.shape) * 2 - 1) * dhash_intensity * 25
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for c in range(img_array.shape[2]):
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img_array[:,:,c][mask[:,:,c]] += delta[:,:,c][mask[:,:,c]]
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else: # 灰度图像
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delta = (np.random.random(img_array.shape) * 2 - 1) * dhash_intensity * 25
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img_array[mask] += delta[mask]
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img_array = np.clip(img_array, 0, 255).astype(np.uint8)
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# 4. 颜色直方图扰动
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image = Image.fromarray(img_array)
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image = self.perturb_color_histogram(image, strength=dhash_intensity)
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# 5. 区域翻转 - 特别有效对抗所有哈希算法
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if strength != "low" and random.random() < 0.5:
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img_array = np.array(image)
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# 随机选择一个小区域
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region_w = random.randint(w//30, w//20)
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region_h = random.randint(h//30, h//20)
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x = random.randint(0, w - region_w)
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y = random.randint(0, h - region_h)
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# 对区域进行水平或垂直翻转
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if random.random() < 0.5: # 水平翻转
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if len(img_array.shape) == 3: # 彩色图像
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img_array[y:y+region_h, x:x+region_w, :] = img_array[y:y+region_h, x:x+region_w, :][:, ::-1, :]
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else: # 灰度图像
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img_array[y:y+region_h, x:x+region_w] = img_array[y:y+region_h, x:x+region_w][:, ::-1]
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else: # 垂直翻转
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if len(img_array.shape) == 3: # 彩色图像
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img_array[y:y+region_h, x:x+region_w, :] = img_array[y:y+region_h, x:x+region_w, :][::-1, :, :]
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else: # 灰度图像
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img_array[y:y+region_h, x:x+region_w] = img_array[y:y+region_h, x:x+region_w][::-1, :]
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image = Image.fromarray(img_array)
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return image
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def optimized_process_image(
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def optimized_process_image(
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self,
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self,
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image: Image.Image,
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image: Image.Image,
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@ -583,20 +704,8 @@ class PosterNotesCreator:
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# 5. 新增 - 应用反查重技术
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# 5. 新增 - 应用反查重技术
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# 根据变化强度选择性应用
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# 根据变化强度选择性应用
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if use_extra:
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if use_extra:
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# 随机决定应用哪些反查重技术
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# 使用综合优化的哈希对抗方法
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apply_phash = random.random() < 0.7
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processed_image = self.optimize_anti_hash_methods(processed_image, variation_strength)
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apply_color = random.random() < 0.7
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# 感知哈希干扰 (在中高强度变化时应用)
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if apply_phash and variation_strength != "low":
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phash_intensity = 0.05 if variation_strength == "medium" else 0.08
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processed_image = self.add_phash_noise(processed_image, phash_intensity)
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# 颜色直方图扰动
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if apply_color:
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color_strength = 0.02 if variation_strength == "low" else \
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0.04 if variation_strength == "medium" else 0.06
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processed_image = self.perturb_color_histogram(processed_image, color_strength)
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# 应用额外效果 (只在需要时)
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# 应用额外效果 (只在需要时)
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if use_extra:
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if use_extra:
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