# coding=utf-8 # attack on the watermark import cv2 import numpy as np import warnings def cut_att3(input_filename=None, input_img=None, output_file_name=None, loc_r=None, loc=None, scale=None): # 剪切攻击 + 缩放攻击 if input_filename: input_img = cv2.imread(input_filename) if loc is None: h, w, _ = input_img.shape x1, y1, x2, y2 = int(w * loc_r[0][0]), int(h * loc_r[0][1]), int(w * loc_r[1][0]), int(h * loc_r[1][1]) else: x1, y1, x2, y2 = loc # 剪切攻击 output_img = input_img[y1:y2, x1:x2].copy() # 如果缩放攻击 if scale and scale != 1: h, w, _ = output_img.shape output_img = cv2.resize(output_img, dsize=(round(w * scale), round(h * scale))) else: output_img = output_img if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img cut_att2 = cut_att3 def resize_att(input_filename=None, input_img=None, output_file_name=None, out_shape=(500, 500)): # 缩放攻击：因为攻击和还原都是缩放，所以攻击和还原都调用这个函数 if input_filename: input_img = cv2.imread(input_filename) output_img = cv2.resize(input_img, dsize=out_shape) if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def bright_att(input_filename=None, input_img=None, output_file_name=None, ratio=0.8): # 亮度调整攻击，ratio应当多于0 # ratio>1是调得更亮，ratio<1是亮度更暗 if input_filename: input_img = cv2.imread(input_filename) output_img = input_img * ratio output_img[output_img > 255] = 255 if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def shelter_att(input_filename=None, input_img=None, output_file_name=None, ratio=0.1, n=3): # 遮挡攻击：遮挡图像中的一部分 # n个遮挡块 # 每个遮挡块所占比例为ratio if input_filename: output_img = cv2.imread(input_filename) else: output_img = input_img.copy() input_img_shape = output_img.shape for i in range(n): tmp = np.random.rand() * (1 - ratio) # 随机选择一个地方，1-ratio是为了防止溢出 start_height, end_height = int(tmp * input_img_shape[0]), int((tmp + ratio) * input_img_shape[0]) tmp = np.random.rand() * (1 - ratio) start_width, end_width = int(tmp * input_img_shape[1]), int((tmp + ratio) * input_img_shape[1]) output_img[start_height:end_height, start_width:end_width, :] = 255 if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def salt_pepper_att(input_filename=None, input_img=None, output_file_name=None, ratio=0.01): # 椒盐攻击 if input_filename: input_img = cv2.imread(input_filename) input_img_shape = input_img.shape output_img = input_img.copy() for i in range(input_img_shape[0]): for j in range(input_img_shape[1]): if np.random.rand() < ratio: output_img[i, j, :] = 255 if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def rot_att(input_filename=None, input_img=None, output_file_name=None, angle=45): # 旋转攻击 if input_filename: input_img = cv2.imread(input_filename) rows, cols, _ = input_img.shape M = cv2.getRotationMatrix2D(center=(cols / 2, rows / 2), angle=angle, scale=1) output_img = cv2.warpAffine(input_img, M, (cols, rows)) if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def cut_att_height(input_filename=None, input_img=None, output_file_name=None, ratio=0.8): warnings.warn('will be deprecated in the future, use att.cut_att2 instead') # 纵向剪切攻击 if input_filename: input_img = cv2.imread(input_filename) input_img_shape = input_img.shape height = int(input_img_shape[0] * ratio) output_img = input_img[:height, :, :] if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def cut_att_width(input_filename=None, input_img=None, output_file_name=None, ratio=0.8): warnings.warn('will be deprecated in the future, use att.cut_att2 instead') # 横向裁剪攻击 if input_filename: input_img = cv2.imread(input_filename) input_img_shape = input_img.shape width = int(input_img_shape[1] * ratio) output_img = input_img[:, :width, :] if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img def cut_att(input_filename=None, output_file_name=None, input_img=None, loc=((0.3, 0.1), (0.7, 0.9)), resize=0.6): warnings.warn('will be deprecated in the future, use att.cut_att2 instead') # 截屏攻击 = 裁剪攻击 + 缩放攻击 + 知道攻击参数（按照参数还原） # 裁剪攻击：其它部分都补0 if input_filename: input_img = cv2.imread(input_filename) output_img = input_img.copy() shape = output_img.shape x1, y1, x2, y2 = shape[0] * loc[0][0], shape[1] * loc[0][1], shape[0] * loc[1][0], shape[1] * loc[1][1] output_img[:int(x1), :] = 255 output_img[int(x2):, :] = 255 output_img[:, :int(y1)] = 255 output_img[:, int(y2):] = 255 if resize is not None: # 缩放一次，然后还原 output_img = cv2.resize(output_img, dsize=(int(shape[1] * resize), int(shape[0] * resize)) ) output_img = cv2.resize(output_img, dsize=(int(shape[1]), int(shape[0]))) if output_file_name is not None: cv2.imwrite(output_file_name, output_img) return output_img # def cut_att2(input_filename=None, input_img=None, output_file_name=None, loc_r=((0.3, 0.1), (0.9, 0.9)), scale=1.1): # # 截屏攻击 = 剪切攻击 + 缩放攻击 + 不知道攻击参数 # if input_filename: # input_img = cv2.imread(input_filename) # h, w, _ = input_img.shape # x1, y1, x2, y2 = int(w * loc_r[0][0]), int(h * loc_r[0][1]), int(w * loc_r[1][0]), int(h * loc_r[1][1]) # # output_img = cut_att3(input_img=input_img, output_file_name=output_file_name, # loc=(x1, y1, x2, y2), scale=scale) # return output_img, (x1, y1, x2, y2) def anti_cut_att_old(input_filename, output_file_name, origin_shape): warnings.warn('will be deprecated in the future') # 反裁剪攻击：复制一块范围，然后补全 # origin_shape 分辨率与约定理解的是颠倒的，约定的是列数*行数 input_img = cv2.imread(input_filename) output_img = input_img.copy() output_img_shape = output_img.shape if output_img_shape[0] > origin_shape[0] or output_img_shape[0] > origin_shape[0]: print('裁剪打击后的图片，不可能比原始图片大，检查一下') return # 还原纵向打击 while output_img_shape[0] < origin_shape[0]: output_img = np.concatenate([output_img, output_img[:origin_shape[0] - output_img_shape[0], :, :]], axis=0) output_img_shape = output_img.shape while output_img_shape[1] < origin_shape[1]: output_img = np.concatenate([output_img, output_img[:, :origin_shape[1] - output_img_shape[1], :]], axis=1) output_img_shape = output_img.shape cv2.imwrite(output_file_name, output_img) def anti_cut_att(input_filename=None, input_img=None, output_file_name=None, origin_shape=None): warnings.warn('will be deprecated in the future, use att.cut_att2 instead') # 反裁剪攻击：补0 # origin_shape 分辨率与约定理解的是颠倒的，约定的是列数*行数 if input_filename: input_img = cv2.imread(input_filename) output_img = input_img.copy() output_img_shape = output_img.shape if output_img_shape[0] > origin_shape[0] or output_img_shape[0] > origin_shape[0]: print('裁剪打击后的图片，不可能比原始图片大，检查一下') return # 还原纵向打击 if output_img_shape[0] < origin_shape[0]: output_img = np.concatenate( [output_img, 255 * np.ones((origin_shape[0] - output_img_shape[0], output_img_shape[1], 3))] , axis=0) output_img_shape = output_img.shape if output_img_shape[1] < origin_shape[1]: output_img = np.concatenate( [output_img, 255 * np.ones((output_img_shape[0], origin_shape[1] - output_img_shape[1], 3))] , axis=1) if output_file_name: cv2.imwrite(output_file_name, output_img) return output_img