🖼️ Recovering Hidden Images (Grayscale & RGB) Using LSB — Complete Q&A Guide

[Rajeev Bagra]

 

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🔰 Initial Context (Read Before the Q&A)

This topic comes from image steganography, a technique used to hide one image inside another.

The hidden image is embedded inside the Least Significant Bits (LSBs) of pixel values.
Because LSB changes alter pixel intensity by only ±1, the visible image looks unchanged to the human eye.

Your task is to:

1. Extract hidden data from the LSBs
2. Rebuild the hidden image
3. Rescale pixel values so the image becomes visible

This guide explains both grayscale (BW) and RGB image recovery in one place.

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📌 Background You Must Know

What is an 8-bit image?

An 8-bit image uses 8 binary digits per channel:

[
2^8 = 256 \Rightarrow \text{pixel values range from } 0 \text{ to } 255
]

• 0 → black
• 255 → white

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Pixel representation

Image Type	Pixel Representation
Grayscale (BW)	p
RGB	(r, g, b)

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🧩 Helper Function (Given)

def extract_end_bits(num_end_bits, pixel):
    return pixel % (2 ** num_end_bits)

For this problem:

• We always use num_end_bits = 1
• Output is either 0 or 1

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🧪 Core Idea (Same for BW & RGB)

1. Extract LSB(s)
2. Get values 0 or 1
3. Rescale:
   • 0 → 0
   • 1 → 255
4. Build a new image

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🧠 Combined Q&A (Grayscale + RGB)

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Q1. What does “recovering a hidden image” mean?

Answer:
It means extracting a secret image that has been invisibly embedded inside another image by using the least significant bits of pixel values.

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Q2. Why are LSBs used to hide images?

Answer:
Because changing an LSB:

• Alters pixel value by only ±1
• Does not create visible distortion
• Is ideal for hiding information securely

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Q3. How is data stored in a grayscale (BW) image?

Answer:
Each grayscale pixel stores one 8-bit value.
The hidden image is stored in the LSB of that value.

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Q4. How is data stored in an RGB image?

Answer:
Each RGB pixel stores three 8-bit values:

• Red channel
• Green channel
• Blue channel

Each channel hides one LSB, so one RGB pixel can hide three bits.

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Q5. Why does extracting LSBs produce a very dark image?

Answer:
Because the extracted values are only 0 or 1, which lie at the very bottom of the brightness range.

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Q6. Why do we rescale 1 → 255?

Answer:
To stretch the recovered image across the full intensity range, making details visible to the human eye.

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Q7. What is the difference in recovery logic between BW and RGB?

Answer:

Step	Grayscale	RGB
Values per pixel	1	3
LSBs extracted	1	3
Output pixel	0 or 255	(0/255, 0/255, 0/255)

The logic is identical; RGB simply repeats it three times per pixel.

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Q8. How do we recover a grayscale image? (Conceptual Code)

def reveal_bw_image(filename):
    img = Image.open(filename).convert("L")
    w, h = img.size
    out = Image.new("L", (w, h))

    for x in range(w):
        for y in range(h):
            bit = extract_end_bits(1, img.getpixel((x, y)))
            out.putpixel((x, y), 255 if bit else 0)

    return out

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Q9. How do we recover an RGB image? (Conceptual Code)

def reveal_rgb_image(filename):
    img = Image.open(filename).convert("RGB")
    w, h = img.size
    out = Image.new("RGB", (w, h))

    for x in range(w):
        for y in range(h):
            r, g, b = img.getpixel((x, y))
            out.putpixel((x, y), (
                255 if extract_end_bits(1, r) else 0,
                255 if extract_end_bits(1, g) else 0,
                255 if extract_end_bits(1, b) else 0
            ))

    return out

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Q10. How should reveal_image handle both cases?

Answer:
reveal_image should:

• Inspect image mode
• Call:
  • reveal_bw_image for "L"
  • reveal_rgb_image for "RGB"
• Return the recovered PIL.Image

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Q11. Should the recovered image’s type match the original?

Answer:
Yes.

• Grayscale → Grayscale output
• RGB → RGB output

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Q12. Where is this technique used in real life?

Answer:

• Digital watermarking
• Copyright protection
• Secure image communication
• Forensics
• Academic image processing

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✅ Final Summary

• 8-bit images use values 0–255
• Hidden data lives in LSBs
• Grayscale → 1 bit per pixel
• RGB → 3 bits per pixel
• Rescaling is essential
• Same logic, different channels

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