Anti-blur tools, history and research

Images can be blurred in many ways. Linear filter pixelation for pixel blocks is just one option. Most blur algorithms tend to blend / stretch pixels as they try to simulate natural blur that is caused by camera movement or defocusing.

there is a bunch of instruments for sharpening for common tasks such as sharpening photographs. Unfortunately, it is with passwords that a different approach is needed. Here the height of the characters is only a couple of blocks, so there is no point in just sharpening, writes the author of the Depix program.

Above, we have provided links to some of the tools and studies published on Habré since 2012.

Recent developments in artificial intelligence have spawned bizarre headlines like “Researchers Have Created a Tool That Perfectly Sharpens Faces.” The illustration below shows examples from scientific article describing the PULSE algorithm researchers from Duke University (USA).

Operation of the PULSE algorithm from Duke University

But in fact, the AI ​​is not here restores photos, and generates new images that are blurred into the same pixels. The foundation of these works is laid by the algorithm RAISR from 2016. AI generates faces that are blurred into the image given at the input. It is important to understand that the generated face is not the original from which the original blur was obtained.

Algorithms like PULSE seem new, but they have a very long history of blur removal tools. Back in 1994 (!) Mark Bouyer from the Southwest Research Institute (USA) wrote a program for generating “Pluto”, blurring pictures and comparing them with real photographs from the Hubble telescope.

Number recovery

In the well-known article 2006 by Dhira Venkatraman explains the algorithm how to recover a pixelated credit card number. The idea is simple: generate all the credit card numbers, pixelate them – and compare the result to the pixelated number.

For example, we see on the Internet a photograph of a check or bank card with a blurred number. As you can see, here we used a linear filter for 8×8 pixel blocks for blurring:

How can these numbers be restored?

1. Take a sample of a blank blank.

2. The script generates pictures for all numbers.

3. Blur each image based on the sample of the original image.

4. Determine the brightness vector of each image. Type vector $$ contains the brightness values ​​of each block.

Here the check number 0000001 corresponds to the vector a(0000001)=[213,201,190,...]…

We also determine the brightness vector of the sample $$…

5. Find the vector with the minimum distance from the original (after normalization).

Normalization constants:

Calculating distance:

For example:

d(0000001) = 1.9363
d(0000002) = 1.9373
...
d(1124587) = 0.12566
d(1124588) = 0.00000

So we find the check number: 1124588…

In 2019 Somdev Sangwan described An interesting method for restoring blurred faces in OSINT investigations. The method is as follows: the resolution of the photo is increased in Photoshop. It gets blurred first:

And then a search is launched on Yandex images (more advanced than Google Images). In this case, Yandex performs “brute-force” of the face in the image:

It is easy to see that all the described methods have something in common. If there is not enough information to correctly restore the image, then we pixelate the similar data – and check if they match.

This is the basis for our algorithm for recovering passwords from screenshots.

Description of the password recovery algorithm

Linear filter for blocks is a deterministic algorithm, then pixelation of the same values ​​always results in the same block. You can try to restore the text in approximately the same way as the numbers in the example above. Each block or combination of blocks can be considered a sub-task.

The algorithm has certain limitations. It requires the same size and color of text on the same background. Modern text editors also add hue and saturation, allowing for a huge number of possible font variations in the screenshot.

Here’s a pretty simple solution. We take de Bruijn sequence for the expected symbols, paste it into the same editor and take a screenshot. This screenshot is used as a wildcard image for similar blocks:

This sequence includes all two-character combinations. It is important to use exactly twocharacter combinations because some pixel blocks cover more than one character.

For the search to work, a block with exactly the same pixel configuration is required. For example, in the test image, the algorithm could not find a part of the letter ‘o’, because in the generated image this block also included a part of the next letter, but in the original image it was clean.

Creating a de Bruijn sequence with spaces around, obviously, creates the same problem, just the opposite: the algorithm will not be able to find the correct blocks, where the adjacent letter hit the edge of the block. You can generate an image with all the letter combinations, and with empty spaces around the edges. For such a picture, the search will take longer, but will give better results.

For most blurry images, the tool finds single match results for blocks. Then it is verified that the matches of the surrounding blocks are at the same geometric distance as in the blurred image.

After going through all the blocks, the program outputs all the correct blocks directly, and for blocks with multiple matches, it outputs the average. The output isn’t perfect, but it works pretty well. The figure shows a test image with random characters. Most of the symbols are readable.

Depix source code published on Github…

By the way, the described technique has something in common with some well-known cryptographic attacks. For example, it looks like cracking hashes, resembles an attack on the ECB block cipher, and plaintext attack (KPA).

So if you want to remove information from the screenshot, delete it completely, flooding with solid color… Although even here you need to think with your head…

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