The secret of the last 2 significant bits

Steganography is a way of hiding secret messages inside of an image. As the amount of steganography increases, so do the attempts of people trying to find these secret messages. The simplest form of digital image steganography is “least significant bit” which is when you can change the lowest bits to be the secret message inside the PNG. This will create a very small change in the way the image looks because only the lowest bits are altered. In images, typically you’ll only change the last 1 or 2 bits if you’re trying to get a lot of data into the image. 

Prompt: How can steganography be used to hide text in an image by modifying the least significant bit?

GPT-4 Response: Steganography is the practice of concealing a message within another non-secret message or data. One common method of steganography involves hiding text within an image by modifying the least significant bit (LSB) of the image pixels.

Here’s a simplified explanation of how this process works:

• Binary Representation: Digital images are composed of pixels, each represented by binary values (usually in RGB format). Each pixel’s color is represented by a combination of red, green, and blue values.
• Least Significant Bit: The least significant bit (LSB) is the rightmost bit in a binary number. Modifying the LSB of each color channel of a pixel slightly alters its color value.
• Embedding Text: To hide text within an image, you can replace the LSB of each color channel of selected pixels with bits from the text message you want to hide. This modification is usually done in a way that’s imperceptible to the human eye, ensuring the image looks visually unchanged.
• Retrieving Text: To retrieve the hidden text from the steganographic image, you simply need to extract the LSBs of the color channels from the pixels where the text was hidden. By reconstructing these bits, you can recover the original text message.

Remember that while LSB steganography is relatively simple and easy to implement, it’s also quite vulnerable to detection if someone suspects that steganography is being used and carefully analyzes the image.

• Critically examine the explanation from Copilot/GPT4:

The above response from GPT-4 does a good job of explaining and visualizing the process of steganography, however the average person would need to use further prompts to understand how the process exactly works. For instance, this response requires prior knowledge and understanding of binary numbers and python code, which to the untrained eye, would not fully visualize the effect steganography has on an image or how it can be used. For instance a common prompt for the average person unfamiliar with these principles would be to inquire about binary numbers. 

Prompt: What is a binary number?

GPT-4: A binary number is a number expressed in the base-2 numeral system. Unlike our everyday decimal (base-10) system, which uses digits from 0 to 9, the binary system only uses two digits: 0 and 1. 

About 80% of my analysis comes from in-class videos and notes while only 20% of my analysis comes from chat gpt or copilot.