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Image spam

Image spam is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Image spam rather than just read about it. In short: Image-based spam, or image spam, is a kind of email spam where the textual spam message is embedded into images, that are then attached to spam emails. Since most of the email clients will display the image file directly to the user, the spam message is conveyed as soon as the email is opened (there is no need to further open the attached image file).

Image spam — main illustration
Image spam — illustration

Key takeaways

  • Image spam belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Image spam to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Image spam from memory before moving on to harder problems.

Reference excerpt

Image-based spam, or image spam, is a kind of email spam where the textual spam message is embedded into images, that are then attached to spam emails. Since most of the email clients will display the image file directly to the user, the spam message is conveyed as soon as the email is opened (there is no need to further open the attached image file).

Technique The goal of image spam is clearly to circumvent the analysis of the email’s textual content performed by most spam filters (e.g., SpamAssassin, RadicalSpam, Bogofilter, SpamBayes). Accordingly, for the same reason, together with the attached image, often spammers add some “bogus” text to the email, namely, a number of words that are most likely to appear in legitimate emails and not in spam. The earlier image spam emails contained spam images in which the text was clean and easily readable, as shown in Fig. 1.

Detection Consequently, optical character recognition tools were used to extract the text embedded into spam images, which could be then processed together with the text in the email’s body by the spam filter, or, more generally, by more sophisticated text categorization techniques. Further, signatures (e.g., MD5 hashing) were also generated to easily detected and block already known spam images. Spammers in turn reacted by applying some obfuscation techniques to spam images, similarly to CAPTCHAs, both to prevent the embedded text to be read by OCR tools, and to mislead signature-based detection. Some examples are shown in Fig. 2. This raised the issue of improving image spam detection using computer vision and pattern recognition techniques. In particular, several authors investigated the possibility of recognizing image spam with obfuscated images by using generic low-level image features (like number of colours, prevalent colour coverage, image aspect ratio, text area), image metadata, etc. (see for a comprehensive survey). Notably, some authors also tried detecting the presence of text in attached images with artifacts denoting an adversarial attempt to obfuscate it.

History Image spam started in 2004 and peaked at the end of 2006, when over 50% of spam was image spam. In mid-2007, it started declining, and practically disappeared in 2008. The reason behind this phenomenon is not easy to understand. The decline of image spam can probably be attributed both to the improvement of the proposed countermeasures (e.g., fast image spam detectors based on visual features), and to the higher requirements in terms of bandwidth of image spam that force spammers to send a smaller amount of spam over a given time interval. Both factors might have made image spam less convenient for spammers than other kinds of spam. Nevertheless, at the end of 2011 a rebirth of image spam was detected, and image spam reached 8% of all spam traffic, albeit for a small period.

See also Anti-spam techniques Email spam

References

Illustrations

Image spam: Fig. 1. Example of a clean spam image
Fig. 1. Example of a clean spam image
Image spam: Fig. 2. Examples of obfuscated spam images to evade OCR-based and signature-based detection
Fig. 2. Examples of obfuscated spam images to evade OCR-based and signature-based detection
Image spam: Fig. 3. Average size of spam versus percentage of image spam[1]
Fig. 3. Average size of spam versus percentage of image spam[1]
Image spam: Fig. 4. Average size of spam versus percentage of image and ZIP/RAR spam (2011-2012, per week)[2]
Fig. 4. Average size of spam versus percentage of image and ZIP/RAR spam (2011-2012, per week)[2]

Worked examples

Example 1 — a first encounter with Image spam

Start with the simplest possible case. Write down what Image spam claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Image spam before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Image spam ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Image spam

In research
Image spam appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Image spam in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Image spam is common in secondary-school and first-year university syllabi. It links to neighbouring topics Email, Spamming, so understanding it makes those chapters shorter.
In everyday life
Look for Image spam outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Image spam in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Image spam means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Image spam out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Image spam in simple terms?

Image-based spam, or image spam, is a kind of email spam where the textual spam message is embedded into images, that are then attached to spam emails. Since most of the email clients will display the image file directly to the user, the spam message is conveyed as soon as the email is opened (ther…

Why does Image spam matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Image spam?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Image spam.

Tags

  • Email
  • Spamming

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