增大了hash干扰强度
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0a1ba3e980
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@ -11,6 +11,15 @@ from PIL import ImageEnhance, ImageFilter
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from .output_handler import OutputHandler
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from .output_handler import OutputHandler
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import io
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import io
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# 尝试导入 scipy，如果失败则标记
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try:
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from scipy.fftpack import dct, idct
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SCIPY_AVAILABLE = True
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except ImportError:
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SCIPY_AVAILABLE = False
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dct = None
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idct = None
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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class PosterNotesCreator:
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class PosterNotesCreator:
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@ -348,10 +357,98 @@ class PosterNotesCreator:
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logger.error(traceback.format_exc())
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logger.error(traceback.format_exc())
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return None
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return None
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def add_dct_noise(self, image: Image.Image, intensity: float = 0.1, block_size: int = 8) -> Image.Image:
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"""
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在DCT域添加噪声以对抗pHash (需要Scipy)
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Args:
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image: 输入图像 (建议传入灰度图或处理亮度通道)
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intensity: 噪声强度 (0-1)
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block_size: DCT块大小 (通常为8)
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Returns:
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添加噪声后的图像
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"""
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if not SCIPY_AVAILABLE:
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logger.warning("Scipy 未安装，无法执行DCT噪声注入。请运行 'pip install scipy'")
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# 可以选择返回原图，或执行一个简化的备用方案
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# 这里我们返回原图
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return image
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try:
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# 确保是灰度图或提取亮度通道 (这里以灰度为例)
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if image.mode != 'L':
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# 如果是彩色图，可以在 Y 通道 (亮度) 操作
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# 为了简化，我们先转为灰度处理
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gray_image = image.convert('L')
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else:
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gray_image = image
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img_array = np.array(gray_image, dtype=float)
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h, w = img_array.shape
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# 确保尺寸是块大小的倍数
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h_pad = (block_size - h % block_size) % block_size
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w_pad = (block_size - w % block_size) % block_size
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if h_pad != 0 or w_pad != 0:
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img_array = np.pad(img_array, ((0, h_pad), (0, w_pad)), mode='reflect')
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padded_h, padded_w = img_array.shape
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else:
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padded_h, padded_w = h, w
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# 分块处理
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for y in range(0, padded_h, block_size):
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for x in range(0, padded_w, block_size):
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block = img_array[y:y+block_size, x:x+block_size]
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# 执行2D DCT
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dct_block = dct(dct(block.T, norm='ortho').T, norm='ortho')
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# 在非DC系数上添加噪声 (跳过 dct_block[0, 0])
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# 噪声强度与系数幅度相关，避免在小系数上加过大噪声
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noise = np.random.randn(block_size, block_size) * intensity * np.abs(dct_block)
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# noise = np.random.uniform(-intensity*50, intensity*50, (block_size, block_size))
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noise[0, 0] = 0 # 不改变DC系数
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# 将噪声添加到DCT系数
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noisy_dct_block = dct_block + noise
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# 执行2D IDCT
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idct_block = idct(idct(noisy_dct_block.T, norm='ortho').T, norm='ortho')
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# 将处理后的块放回图像数组
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img_array[y:y+block_size, x:x+block_size] = idct_block
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# 裁剪回原始尺寸 (如果有填充)
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if h_pad != 0 or w_pad != 0:
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img_array = img_array[:h, :w]
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# 裁剪像素值并转换类型
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img_array = np.clip(img_array, 0, 255)
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modified_gray = Image.fromarray(img_array.astype(np.uint8))
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# 如果原图是彩色，将修改后的亮度通道合并回去
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if image.mode == 'RGB' and gray_image is not image:
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# 注意：简单替换亮度通道可能效果不好，混合通常更好
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# 这里用混合的方式
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blend_factor = 0.3 # 控制混合强度
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r, g, b = image.split()
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r = Image.blend(r, modified_gray, blend_factor)
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g = Image.blend(g, modified_gray, blend_factor)
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b = Image.blend(b, modified_gray, blend_factor)
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return Image.merge('RGB', (r, g, b))
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else:
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# 如果原图是灰度或处理失败，返回修改后的灰度图
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return modified_gray
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except Exception as e:
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logger.error(f"DCT噪声注入出错: {e}")
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return image # 出错时返回原图
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def add_phash_noise(self, image: Image.Image, intensity: float = 0.05) -> Image.Image:
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def add_phash_noise(self, image: Image.Image, intensity: float = 0.05) -> Image.Image:
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"""
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"""
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添加扰动以对抗感知哈希算法(pHash)
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添加扰动以对抗感知哈希算法(pHash)
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通过在频域添加低频扰动实现
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现在调用基于 Scipy 的 DCT 噪声注入方法
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Args:
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Args:
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image: 输入图像
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image: 输入图像
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@ -360,238 +457,126 @@ class PosterNotesCreator:
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Returns:
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Returns:
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添加扰动后的图像
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添加扰动后的图像
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"""
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"""
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# 灰度化处理
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return self.add_dct_noise(image, intensity=intensity)
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gray_image = image.convert('L')
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width, height = gray_image.size
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# 确保宽高是8的倍数(DCT通常用8x8块)
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new_width = (width // 8) * 8
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new_height = (height // 8) * 8
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if new_width != width or new_height != height:
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gray_image = gray_image.resize((new_width, new_height))
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# 转为numpy数组
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img_array = np.array(gray_image)
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# 简化版DCT域扰动
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# 分块处理图像
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for y in range(0, new_height, 8):
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for x in range(0, new_width, 8):
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block = img_array[y:y+8, x:x+8].astype(float)
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# 简单DCT - 对块应用频域变化
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# 这里使用简单方法模拟DCT效果
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# 真正的DCT需要使用scipy.fftpack
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avg = np.mean(block)
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# 修改低频区块(除直流分量外)
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noise_value = random.uniform(-intensity * 10, intensity * 10)
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# 扰动左上角的低频系数(类似于DCT中的低频区域)
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block[1:3, 1:3] += noise_value
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# 应用回原图
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img_array[y:y+8, x:x+8] = np.clip(block, 0, 255)
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# 转回PIL图像
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modified_image = Image.fromarray(img_array.astype(np.uint8))
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# 调整回原始大小
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if new_width != width or new_height != height:
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modified_image = modified_image.resize((width, height), Image.LANCZOS)
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# 将修改后的灰度通道应用到原彩色图像
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if image.mode == 'RGB':
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r, g, b = image.split()
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# 混合原始图像与修改过的灰度图
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blend_factor = 0.2 # 混合强度
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r = Image.blend(r, modified_image, blend_factor)
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g = Image.blend(g, modified_image, blend_factor)
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b = Image.blend(b, modified_image, blend_factor)
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return Image.merge('RGB', (r, g, b))
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else:
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return modified_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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Args:
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image: 输入图像
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strength: 扰动强度(0-1)
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Returns:
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处理后的图像
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"""
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# 确保为RGB模式
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if image.mode != 'RGB':
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image = image.convert('RGB')
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# 转为numpy数组
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img_array = np.array(image)
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height, width, channels = img_array.shape
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# 对每个通道分别处理
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for channel in range(channels):
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# 计算当前通道的直方图
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hist, _ = np.histogram(img_array[:,:,channel].flatten(), bins=64, range=(0, 256))
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# 找出主要颜色区间 (频率高的区间)
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threshold = np.percentile(hist, 70) # 取前30%的颜色块
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significant_bins = np.where(hist > threshold)[0]
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if len(significant_bins) > 0:
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for bin_idx in significant_bins:
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# 计算当前bin对应的颜色范围
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bin_width = 256 // 64
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color_low = bin_idx * bin_width
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color_high = (bin_idx + 1) * bin_width
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# 创建颜色范围掩码
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mask = (img_array[:,:,channel] >= color_low) & (img_array[:,:,channel] < color_high)
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if np.any(mask):
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# 生成随机偏移值
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offset = int(strength * bin_width * (random.random() - 0.5) * 2)
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# 应用偏移，确保在0-255范围内
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img_array[:,:,channel][mask] = np.clip(
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img_array[:,:,channel][mask] + offset, 0, 255).astype(np.uint8)
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# 转回PIL图像
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return Image.fromarray(img_array)
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def strip_metadata(self, image: Image.Image) -> Image.Image:
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"""
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移除图像中的所有元数据
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Args:
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image: 输入图像
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Returns:
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无元数据的图像
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"""
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# 创建无元数据的副本
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data = io.BytesIO()
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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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def optimize_anti_hash_methods(self, image: Image.Image, strength: str = "medium") -> Image.Image:
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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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"""
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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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综合优化的哈希对抗方法，强度已增加
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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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"""
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# 根据强度设置参数
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# 根据强度设置参数 (显著增加 high 强度)
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if strength == "low":
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if strength == "low":
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ahash_intensity = 0.03
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ahash_intensity = 0.03
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phash_intensity = 0.04
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phash_intensity = 0.05 # 基础DCT噪声强度
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dhash_intensity = 0.03
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dhash_intensity = 0.03
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region_flip_prob = 0.3
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num_ahash_blocks = random.randint(8, 15)
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num_dhash_lines = random.randint(6, 10)
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elif strength == "high":
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elif strength == "high":
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ahash_intensity = 0.09
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ahash_intensity = 0.18 # 大幅增加
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phash_intensity = 0.08
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phash_intensity = 0.15 # 大幅增加
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dhash_intensity = 0.09
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dhash_intensity = 0.18 # 大幅增加
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region_flip_prob = 0.7 # 更大概率翻转
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num_ahash_blocks = random.randint(20, 35) # 更多块
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num_dhash_lines = random.randint(15, 25) # 更多线
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else: # medium
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else: # medium
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ahash_intensity = 0.06
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ahash_intensity = 0.08 # 增加
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phash_intensity = 0.06
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phash_intensity = 0.08 # 增加
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dhash_intensity = 0.06
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dhash_intensity = 0.08 # 增加
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region_flip_prob = 0.5
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num_ahash_blocks = random.randint(12, 25)
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num_dhash_lines = random.randint(10, 18)
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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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# 1. 针对aHash (平均哈希)的处理 - 强度已增加
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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, dtype=np.int16)
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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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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_ahash_blocks = random.randint(10, 20)
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num_blocks = random.randint(10, 20)
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for _ in range(num_ahash_blocks):
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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_w = random.randint(w//20, w//10)
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block_h = random.randint(h//20, h//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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x = random.randint(0, w - block_w)
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y = random.randint(0, h - block_h)
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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(-35, 35) * ahash_intensity) # 增加delta范围
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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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block = img_array[y:y+block_h, x:x+block_w]
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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] = np.clip(block + delta, 0, 255)
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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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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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# 2. 调用强化的pHash对抗方法
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image = self.add_phash_noise(image, intensity=phash_intensity)
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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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# 3. 针对dHash (差值哈希)的处理 - 强度已增加
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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, dtype=np.int16)
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img_array = np.array(image)
|
h, w = img_array.shape[0], img_array.shape[1]
|
||||||
|
|
||||||
# 计算图像尺寸并创建掩码
|
|
||||||
mask = np.zeros_like(img_array, dtype=bool)
|
mask = np.zeros_like(img_array, dtype=bool)
|
||||||
grid_size = 8 # dHash通常是8x8
|
|
||||||
|
|
||||||
# 在图像中选择8-12条边缘线进行干扰
|
# num_dhash_lines = random.randint(8, 12)
|
||||||
num_lines = random.randint(8, 12)
|
for _ in range(num_dhash_lines):
|
||||||
for _ in range(num_lines):
|
if random.random() < 0.5:
|
||||||
# 随机决定是水平线还是垂直线
|
|
||||||
if random.random() < 0.5: # 水平线
|
|
||||||
y = random.randint(0, h - 1)
|
y = random.randint(0, h - 1)
|
||||||
line_width = random.randint(1, 3)
|
line_width = random.randint(1, 4) # 增加线宽可能性
|
||||||
if len(mask.shape) == 3: # 彩色图像
|
if len(mask.shape) == 3:
|
||||||
mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :, :] = True
|
mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :, :] = True
|
||||||
else: # 灰度图像
|
else:
|
||||||
mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :] = True
|
mask[max(0, y-line_width//2):min(h, y+line_width//2+1), :] = True
|
||||||
else: # 垂直线
|
else:
|
||||||
x = random.randint(0, w - 1)
|
x = random.randint(0, w - 1)
|
||||||
line_width = random.randint(1, 3)
|
line_width = random.randint(1, 4) # 增加线宽可能性
|
||||||
if len(mask.shape) == 3: # 彩色图像
|
if len(mask.shape) == 3:
|
||||||
mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1), :] = True
|
mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1), :] = True
|
||||||
else: # 灰度图像
|
else:
|
||||||
mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1)] = True
|
mask[:, max(0, x-line_width//2):min(w, x+line_width//2+1)] = True
|
||||||
|
|
||||||
# 应用梯度干扰
|
delta = (np.random.random(img_array.shape) * 2 - 1) * dhash_intensity * 35 # 增加delta范围
|
||||||
if len(img_array.shape) == 3: # 彩色图像
|
img_array[mask] += delta[mask].astype(np.int16)
|
||||||
delta = (np.random.random(img_array.shape) * 2 - 1) * dhash_intensity * 25
|
|
||||||
for c in range(img_array.shape[2]):
|
|
||||||
img_array[:,:,c][mask[:,:,c]] += delta[:,:,c][mask[:,:,c]]
|
|
||||||
else: # 灰度图像
|
|
||||||
delta = (np.random.random(img_array.shape) * 2 - 1) * dhash_intensity * 25
|
|
||||||
img_array[mask] += delta[mask]
|
|
||||||
|
|
||||||
img_array = np.clip(img_array, 0, 255).astype(np.uint8)
|
img_array = np.clip(img_array, 0, 255)
|
||||||
|
|
||||||
# 4. 颜色直方图扰动
|
# 4. 颜色直方图扰动 (强度也略微增加)
|
||||||
image = Image.fromarray(img_array)
|
image = Image.fromarray(img_array.astype(np.uint8))
|
||||||
image = self.perturb_color_histogram(image, strength=dhash_intensity)
|
color_hist_strength = dhash_intensity * 0.6 # 关联强度
|
||||||
|
image = self.perturb_color_histogram(image, strength=color_hist_strength)
|
||||||
|
|
||||||
# 5. 区域翻转 - 特别有效对抗所有哈希算法
|
# 5. 区域翻转 - 强度已增加
|
||||||
if strength != "low" and random.random() < 0.5:
|
if random.random() < region_flip_prob:
|
||||||
img_array = np.array(image)
|
img_array = np.array(image)
|
||||||
# 随机选择一个小区域
|
h, w = img_array.shape[0], img_array.shape[1]
|
||||||
region_w = random.randint(w//30, w//20)
|
|
||||||
region_h = random.randint(h//30, h//20)
|
# 增加区域大小可能性
|
||||||
|
max_region_factor = 15 if strength == 'high' else 20
|
||||||
|
region_w = random.randint(w//(max_region_factor+5), w//max_region_factor)
|
||||||
|
region_h = random.randint(h//(max_region_factor+5), h//max_region_factor)
|
||||||
x = random.randint(0, w - region_w)
|
x = random.randint(0, w - region_w)
|
||||||
y = random.randint(0, h - region_h)
|
y = random.randint(0, h - region_h)
|
||||||
|
|
||||||
# 对区域进行水平或垂直翻转
|
# 加入90度旋转的可能性
|
||||||
if random.random() < 0.5: # 水平翻转
|
action = random.choice(['flip_h', 'flip_v', 'rotate_90']) if strength != 'low' else random.choice(['flip_h', 'flip_v'])
|
||||||
if len(img_array.shape) == 3: # 彩色图像
|
|
||||||
img_array[y:y+region_h, x:x+region_w, :] = img_array[y:y+region_h, x:x+region_w, :][:, ::-1, :]
|
|
||||||
else: # 灰度图像
|
|
||||||
img_array[y:y+region_h, x:x+region_w] = img_array[y:y+region_h, x:x+region_w][:, ::-1]
|
|
||||||
else: # 垂直翻转
|
|
||||||
if len(img_array.shape) == 3: # 彩色图像
|
|
||||||
img_array[y:y+region_h, x:x+region_w, :] = img_array[y:y+region_h, x:x+region_w, :][::-1, :, :]
|
|
||||||
else: # 灰度图像
|
|
||||||
img_array[y:y+region_h, x:x+region_w] = img_array[y:y+region_h, x:x+region_w][::-1, :]
|
|
||||||
|
|
||||||
|
region = img_array[y:y+region_h, x:x+region_w]
|
||||||
|
if action == 'flip_h':
|
||||||
|
img_array[y:y+region_h, x:x+region_w] = region[:, ::-1]
|
||||||
|
elif action == 'flip_v':
|
||||||
|
img_array[y:y+region_h, x:x+region_w] = region[::-1, :]
|
||||||
|
elif action == 'rotate_90' and len(img_array.shape) == 3: # 旋转只对原尺寸区域有效
|
||||||
|
# 注意：旋转可能需要调整区域大小或填充，这里简化处理
|
||||||
|
# 仅在区域接近正方形时效果较好
|
||||||
|
if abs(region_w - region_h) < 5:
|
||||||
|
rotated_region = np.rot90(region)
|
||||||
|
# 需要确保旋转后尺寸匹配，如果尺寸变化则跳过或填充
|
||||||
|
if rotated_region.shape[0] == region_h and rotated_region.shape[1] == region_w:
|
||||||
|
img_array[y:y+region_h, x:x+region_w] = rotated_region
|
||||||
|
|
||||||
|
image = Image.fromarray(img_array)
|
||||||
|
|
||||||
|
# 6. (新增可选) 轻微高斯噪声 - 对所有哈希都有轻微普适性干扰
|
||||||
|
if strength != 'low' and random.random() < 0.4:
|
||||||
|
img_array = np.array(image)
|
||||||
|
noise_sigma = 1.0 if strength == 'medium' else 2.0 # 噪声标准差
|
||||||
|
noise = np.random.normal(0, noise_sigma, img_array.shape)
|
||||||
|
img_array = np.clip(img_array + noise, 0, 255).astype(np.uint8)
|
||||||
image = Image.fromarray(img_array)
|
image = Image.fromarray(img_array)
|
||||||
|
|
||||||
return image
|
return image
|
||||||
@ -750,6 +735,70 @@ class PosterNotesCreator:
|
|||||||
|
|
||||||
return processed_image
|
return processed_image
|
||||||
|
|
||||||
|
def perturb_color_histogram(self, image: Image.Image, strength: float = 0.03) -> Image.Image:
|
||||||
|
"""
|
||||||
|
扰动图像的颜色直方图，对抗基于颜色统计的图像匹配
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image: 输入图像
|
||||||
|
strength: 扰动强度(0-1)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
处理后的图像
|
||||||
|
"""
|
||||||
|
# 确保为RGB模式
|
||||||
|
if image.mode != 'RGB':
|
||||||
|
image = image.convert('RGB')
|
||||||
|
|
||||||
|
# 转为numpy数组
|
||||||
|
img_array = np.array(image)
|
||||||
|
height, width, channels = img_array.shape
|
||||||
|
|
||||||
|
# 对每个通道分别处理
|
||||||
|
for channel in range(channels):
|
||||||
|
# 计算当前通道的直方图
|
||||||
|
hist, _ = np.histogram(img_array[:,:,channel].flatten(), bins=64, range=(0, 256))
|
||||||
|
|
||||||
|
# 找出主要颜色区间 (频率高的区间)
|
||||||
|
threshold = np.percentile(hist, 70) # 取前30%的颜色块
|
||||||
|
significant_bins = np.where(hist > threshold)[0]
|
||||||
|
|
||||||
|
if len(significant_bins) > 0:
|
||||||
|
for bin_idx in significant_bins:
|
||||||
|
# 计算当前bin对应的颜色范围
|
||||||
|
bin_width = 256 // 64
|
||||||
|
color_low = bin_idx * bin_width
|
||||||
|
color_high = (bin_idx + 1) * bin_width
|
||||||
|
|
||||||
|
# 创建颜色范围掩码
|
||||||
|
mask = (img_array[:,:,channel] >= color_low) & (img_array[:,:,channel] < color_high)
|
||||||
|
|
||||||
|
if np.any(mask):
|
||||||
|
# 生成随机偏移值
|
||||||
|
offset = int(strength * bin_width * (random.random() - 0.5) * 2)
|
||||||
|
|
||||||
|
# 应用偏移，确保在0-255范围内
|
||||||
|
img_array[:,:,channel][mask] = np.clip(
|
||||||
|
img_array[:,:,channel][mask] + offset, 0, 255).astype(np.uint8)
|
||||||
|
|
||||||
|
# 转回PIL图像
|
||||||
|
return Image.fromarray(img_array)
|
||||||
|
|
||||||
|
def strip_metadata(self, image: Image.Image) -> Image.Image:
|
||||||
|
"""
|
||||||
|
移除图像中的所有元数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image: 输入图像
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
无元数据的图像
|
||||||
|
"""
|
||||||
|
# 创建无元数据的副本
|
||||||
|
data = io.BytesIO()
|
||||||
|
image.save(data, format=image.format if image.format else 'PNG')
|
||||||
|
return Image.open(data)
|
||||||
|
|
||||||
def process_poster_for_notes(
|
def process_poster_for_notes(
|
||||||
run_id: str,
|
run_id: str,
|
||||||
topic_index: int,
|
topic_index: int,
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user