python - HTTP 响应中使用了哪种 base64?

标签 python selenium web-scraping browsermob-proxy browsermob

我正在使用 Browser Mob Proxy(使用 browsermob-proxy Python 包)捕获 Selenium 发出的 HTTP 请求。在我的 HAR 文件中,我看到了这个(它应该是一个 Javascript 文件):

"content": {
        "comment": "",
        "size": 10908,
        "mimeType": "application/x-javascript; charset=utf-8",
        "encoding": "base64",
        "text": "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"
      }

但是,当使用

解码 contenttext
base64.b64decode(my_coded_text).decode("UTF-8")

我总是收到以下错误:

UnicodeDecodeError: 'utf-8' codec can't decode bytes in position 0-1: invalid continuation byte

解码此类字符串的正确方法是什么?

编辑添加

我假设问题来自 rfc1341 的这一行:“base64 数据中的 CRLF 序列应转换为可引用的可打印换行符,但仅在转换文本数据时才有效”,因为解码可以很好地处理图像。

但是,我还没有理解什么是base64数据中的CRLF序列,以及如何转换它。

最佳答案

我不确定“文本”属性中应该包含哪种数据,但肯定不是文本。您需要分析发出此请求的应用程序才能弄清楚这一点。

解码后的文本以以下数据开始,其中不包含任何可读文本,并且与任何已知文件格式都不匹配:

00000000: e2a1 7614 9214 be79 5194 3352 a680 7a29  ..v....yQ.3R..z)
00000010: e064 8850 0375 fe03 f106 0ab6 1351 55cf  .d.P.u.......QU.
00000020: ad32 a1ef d6fb a2a4 96e4 30ab 203e eb8c  .2........0. >..
00000030: e3f1 46dd 155c 2e56 21c4 7987 df94 833f  ..F..\.V!.y....?

数据分析表明它实际上是不可压缩的。这意味着它可能被加密,或者使用未知算法进行压缩。

关于python - HTTP 响应中使用了哪种 base64?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48467245/

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