开源日报 每天推荐一个 GitHub 优质开源项目和一篇精选英文科技或编程文章原文,坚持阅读《开源日报》,保持每日学习的好习惯。
今日推荐开源项目:《流程图 flowy》
今日推荐英文原文:《How To Hide Data in Images Using Python》
开源日报第626期:《流程图 flowy》
今日推荐开源项目:《流程图 flowy》传送门:GitHub链接
推荐理由:众所周知,流程图一般来说内容来来去去都是那几类,而要想画的美观也需要在细节上下不少功夫。这个项目是一个画流程图的 JS 库,将流程图中的各个步骤用卡片的形式来表现,而你只需要关心卡片内容,不用费心于如何整顿诸如连接箭头等等琐碎的细节,尽管现在作为流程图来说功能还不够完善,但是这个想法会让流程图的创建变得更为简单也更为美观。
开源日报第626期:《流程图 flowy》
今日推荐英文原文:《How To Hide Data in Images Using Python》作者:Ashwin Goel
原文链接:https://medium.com/better-programming/image-steganography-using-python-2250896e48b9
推荐理由:传说中百闻不如一见的图片藏数据大法

How To Hide Data in Images Using Python

An introduction to image steganography

Steganography is the art of hiding secret data in any file.

The secret data can be data of any format like text or even a file. In a nutshell, the main motive of steganography is to hide the intended information within any file, usually an image, audio, or video, without actually changing the external appearance of the file, i.e. it should look the same as before.

In this blog, we will be focussing on learning image-based steganography, i.e. hiding secret data in an image.

But before diving a little deeper into it, let’s look at what an image comprises of.
  1. Pixels are the building blocks of an image.
  2. Every pixel contains three values: (red, green, blue) also known as RGB values.
  3. Every RGB value ranges from 0 to 255.
This much information is enough to get started.

Now, let’s look at how we can encode and decode data into our image.

Encoding

There are a lot of algorithms that can be used to encode data into the image, and in fact, you can also make one yourself. The one being used in this blog is easy to understand and implement, as well.

The algorithm is as follows:
  1. For each character in the data, its ASCII value is taken and converted into 8-bit binary [1].
  2. Three pixels are read at a time having a total of 3×3=9 RGB values. The first eight RGB values are used to store one character that is converted into an 8-bit binary.
  3. The corresponding RGB value and binary data are compared. If the binary digit is 1 then the RGB value is converted to odd and, otherwise, even.
  4. The ninth value determines if more pixels should be read or not. If there is more data to be read, i.e. encoded or decoded, then the ninth pixel changes to even. Otherwise, if we want to stop reading pixels further, then make it odd.
Repeat this process until all the data is encoded into the image.

Example

Suppose the message to be hidden is ‘Hii’.

The message is of three bytes, therefore, the pixels required to encode the data are 3 x 3 = 9. Consider a 4 x 3 image with a total of 12 pixels, which are sufficient to encode the given data.

Step 1

The ASCII value of H is 72, whose binary equivalent is 01001000.

Step 2

Read the first three pixels.

Step 3

Now, change the pixel value to odd for 1 and even for 0 as in the binary equivalent of data.

For example, the first binary digit is 0 and the first RGB value is 27, it needs to be converted to odd, which implies 26.

Similarly, 64gets converted to 63because the next binary digit is 1so the RGB value should be made odd.

So, the modified pixels are:

Step 4

Since we have to encode more data, the last value should be even. Similarly, i can be encoded in this image.

While making the pixel values odd/even by doing +1 or -1, you should take care of binary conditions. I.e., the pixel value should be more than or equal to 0 and less than or equal to 255.

The new image will look like:

Decoding

For decoding, we shall try to find how to reverse the previous algorithm that we used to encode data.
  1. Again, three pixels are read at a time. The first 8 RGB values give us information about the secret data, and the ninth value tells us whether to move forward or not.
  2. For the first eight values, if the value is odd, then the binary bit is 1, otherwise it is 0.
  3. The bits are concatenated to a string, and with every three pixels, we get a byte of secret data, which means one character.
  4. Now, if the ninth value is even then we keep reading pixels three at a time, or otherwise, we stop.

For example

Let’s start reading three pixels at a time.

Consider our previously encoded image.

Step 1

We first read the three pixels:

Step 2

Reading the first value: 26, which is even, therefore the binary bit is 0. Similarly, for 63, the binary bit is 1and for 164it is 0. This process continues until the eight RGB value.

Step 3

We finally get the binary value: 01001000 after concatenating all individual binary values. The final binary data corresponds to decimal value 72, and in ASCII, it represents the character H.

Step 4

Since the ninth value is even, we repeat the above steps. We stop when the ninth value encountered is odd.

As a result, we get our original message back which was Hii.

The Python program for the above algorithm is as follows:

The module used in the program is PIL which stands for Python Imaging Library. It gives us the capability to perform operations on images in Python.

Program Execution

开源日报第626期:《流程图 flowy》
开源日报第626期:《流程图 flowy》
开源日报第626期:《流程图 flowy》
Input image
开源日报第626期:《流程图 flowy》
Output image

Limitations

This program might not work as expected with JPEG images because JPEG uses lossy compression which means that the pixels are modified to compress the image and reduce the quality, therefore data loss happens.
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