首页 > 试题广场 >

有一张图灵奖得主的肖像照片,被一个学生用简单的异或加密方法,

[问答题]
有一张图灵奖得主的肖像照片,被一个学生用简单的异或加密方法,编码成了如下这样的字符串:
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

已知肖像照片是64x64像素的0~255级灰度图片,内存中用raw bitmap方式,每个像素用一个字节存储。对肖像照片的原始数据,学生使用的加密代码片段如下(Python3代码,代码中的key值是未知的加密密钥):

_KEY_LEN = 2
bitmap = PIL.Image.open(image_path).tobytes()
encrypted = []
for index, byte in enumerate(bitmap):
   
encrypted.append(byte ^ key[index % _KEY_LEN])
return base64.standard_b64encode(bytes(encrypted))
(1)请问:这张被加密的照片,是以下哪位图灵奖得主的肖像?

        (A) Marvin Minsky

        (B) John L. Hennessy

        (C) Donald E. Knuth

        (D) Raj Reddy

        (E) John McCarthy

        (F) Edsger W. Dijkstra

        (G) John Hopcroft

        (H) Alan Kay


(2)请写出本题解题的主要思路,以及解题时使用的主要代码片段。

 (E) John McCarthy
import base64 
import PIL 
from PIL import Image
res = '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'
encrypted = base64.standard_b64decode(res) 
print(len(encrypted)) 
img = Image.frombytes('L', (64, 64), encrypted) 
img.show()

发表于 2019-04-26 17:12:43 回复(2)

我也不知道是谁,请自行辨识。
哈哈哈!😃😃😃😃😃
发表于 2019-12-06 18:32:58 回复(0)
John McCarthy

import base64 from PIL import Image import numpy as np
string2 = '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' string1 = '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'  codes = base64.standard_b64decode(string2) for i in range(255): for j in range(255):
        afterDecode = [] for index, element in enumerate(codes): if index % 2 == 0:
                afterDecode.append(element ^ i) else:
                afterDecode.append(element ^ j) #分别用两种密钥解密  #print(afterDecode)  for k in range(len(afterDecode)):
            afterDecode[k] = 255 - afterDecode[i]
        arra = np.array(afterDecode).reshape((64, 64)) #print(arra)  pict = Image.fromarray(arra).convert("L") #pict.show()  pict.save("./picture/{}_{}.png".format(i, j), "PNG") #存储到本地

发表于 2019-05-27 20:17:41 回复(0)
求解
发表于 2019-04-11 15:27:47 回复(2)