Anti-Phishing Cybersecurity


Visual Signals: Critical Enhancement to Slam the Door on Scammers

Visual-AI for Anti-Phishing Detection Whitepaper

This whitepaper provides:

  • An analysis of phishing that relies on visual elements to gain the trust of the user
  • A review of how bad actors use graphical techniques and elements to build trust, confuse users and evade detection
  • Details on how Visual-AI could be applied to detect the “undetectable”

Recently updated with new data for 2022! Discover how powerful computer vision can help enhance phishing protection platforms. 

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Detecting Graphical Attack Vectors In Phishing Emails & Spoofed Websites With Visual-AI / Computer Vision – Whitepaper

Phishing is a growing problem

  • 60,000 phishing sites were reported in March 2020 alone – That’s a 160% increase in 5 years.
  • Attackers spoofed the world’s top 200 brands and created 50,000 fake login pages.

Visual-AI + Standard AI is the Answer!

By supplementing the current AI systems with advanced and targeted Visual-AI, you achieve a perfect combination that delivers ‘human’ visual analysis (but at machine speed) with AI data analysis. This combination is far more difficult for bad actors to evade and can be effective at near real-time (one second or less), allowing all content and traffic requests to be processed in real-time to the client.


This Whitepaper concerns itself with any form of phishing that relies on visual elements in the communication to gain the trust of the reader. Such as attacks that leverage brands and those techniques where the content of emails, websites and documents are converted to graphics to fool phishing detection systems (PDS).

The concept we propose is one that operates in near real-time and views content as a human would see it but at ‘machine-speed’. Able to handle massive volumes quickly and accurately. Delivering a valuable scoring system to a PDS, which can then be used in their own filtering and scoring system. This web page contains the key highlights from each section of the whitepaper. For the full document, hit the download button at any point.

Problem Statement

Phishing, in all its forms, has become of critical concern to so many companies and individuals, and no wonder  given that cybercrime is predicted to cost the world $10.5 trillion annually by 2025, up from $3 trillion a  decade ago and $6 trillion in 2021. This has fuelled massive growth in the cybersecurity sector. In fact, global  spending on cybersecurity products and services will grow to $1.75 trillion cumulatively for the five-year  period from 2021 to 2025. All according to Cybersecurity Ventures

One of the fastest growing trends is phishing, which has seen a massive rise since the beginning of the COVID  era. APWG saw 260,642 phishing attacks in July 2021, which was the highest monthly in APWG’s reporting  history. And the overall number of phishing attacks has doubled from early 2020. 

It’s An Arms Race

Anti-phishing companies are in an arms race with bad actors. From their ever more ingenious obfuscation  techniques to avoid detection, to their targeting of more channels (voice, social, malware, web ads and text  messages), and their more recent shift to targeting communications endpoints, especially with the shift to  home-working where defenses are weakest. The list of checks and balances for PDS (Phishing Defence  Systems), grows ever longer and this means the TTC (Time To Confirmation) also grows longer.

Readily Available, Weaponised Technologies

As ingenious as many of the phishing techniques are, being a successful bad actor needs little more than a  good working knowledge of IT systems and access to the dark web. Some of the darker corners of the dark  web are reported to be truly horrifying, but the more common areas of the dark web are a haven for nefarious  commerce. Hacking and phishing ‘kits’ can be purchased from as little as $2, and whole cybercrime solutions  can be bought at what most would consider ‘very reasonable prices’. The suppliers of these solutions spare  no effort in ensuring their clients are happy, providing spoofed pages that imitate real companies and even full  guides on how to launch an email phishing scam. 

Meanwhile, access to whole dark-server based services, such as spam email servers, make it very easy to get up  and running. These are advanced servers that make it hard for junk filters to identify that it’s a phishing email. In  addition, the “From” address in the emails may look legitimate and use a valid domain like 

The Odds Are Stacked Against You

In this arms race, PDS providers can understandably feel like they can’t win. After all, their customers need them to detect and stop every phishing attempt, ensuring that not one single breach is allowed because just one breach can create a million euro/dollar issue. The bad actors, however, have a much lower bar, where just one breach is a major success.

Brute Force Method

A sure-fire method of stopping phishing attacks where the bad actor is using links to a malicious site or file, is to simply block any communication from unknown sources that contain a file attachment or a link to an external site. Don’t worry about checking and testing the content or links, just bounce the email and let genuine senders reach out to try and fix the issue with their intended recipient. 

As effective as this is, it stops a lot of genuine communications and causes issues with systems that send everything from verification links to invoices for services. The recipient doesn’t receive the email and the sender (being automated) never sees the bounce! Clearly, a better solution is required.


The PDS industry faces numerous challenges in delivering a system that can be relied on by their clients:

Time & Speed

Any PDS must operate as close to real-time as possible, with absolute minimal delay, so as to be transparent to the user. Scammers are consummate opportunists and they know that it takes time for relevant data about domains, senders, URLs and other sources/flags to be gathered, scored, and added to relevant blacklists. For this reason, they switch and change often, with sites existing, in some cases, for only hours.

Fake Site Detection Gap

Complex Systems Take Time To Patch

Cyber-security systems have traditionally relied on patches for things like malware signatures and blacklists, but  IT professionals have a natural reluctance to patch, and especially to patch too often. Ignoring the planning and  logistics of implementing patches (often requiring taking servers offline), IT staff hate ‘rocking the boat’. Patches  can cause major outages and introduce other unforeseen issues, which then cause stress and anxiety to resolve.  This introduces delays in closing gaps in the defence cordon, giving bad actors the time they need to get a win. 

Systems need to be able to flag possible threats without the need for constant patching. It should offer its detection functionality outside of the patching-based methodology and offer critical value-add to patching-based defence systems.

Massive Volumes

As shown at the beginning of this document, bad actors can generate 50,000 fake login pages for the world’s  top 200 brands with relative ease. Also, highlighted by the APWG’s (Anti-Phishing Working Group) Phishing  Activity Trends Report for Q3 2021 Phishing attacks are at their highest level in recorded history. The issue,  therefore, that PDS providers have is quickly and efficiently sifting through all communications and links to  validate genuine communications and sites while blocking bad ones. 

Any system that can help filter out the noise and focus on the highest-risk threats will help to deliver timely  detection and blocking in a transparent way. 

Multiple Attack Vectors, Channels And Devices/End-Points

Phishing used to be limited only to email on PC. Today every device and every channel can now be a conduit for phishing. From mobile malware delivered through SMS (SMiShing), the gathering of data through voice calls (Vishing) and even through malicious apps or ads served in apps, websites, and social media.  

Social engineering is one of the latest techniques used. Employing gamification and the power of sharing, they operate through social media sites and chat apps to snare victims, who then share a fake link (which is trusted by other users because it came from a trusted source). Each person then inadvertently installs malware or gives away perhaps minor details that provide the next link the scammers need for their BEC (Business Email Compromise) campaign.  

The era of BYOD (Bring Your Own Device) and home-working also brings challenges, where devices or personal email accounts may not be protected, yet are operating inside the business perimeter. 

The ideal solution must therefore sit behind and between all the devices and channels to check sites and files when accessed inside the perimeter, and do all this in real-time.

Bad Actors Employ Legitimate Technologies

As cloud services become more popular and new techniques are employed by users and made standard by social sites (like Twitter’s shortening of all URLs to versions), bad actors are exploiting the trust that users have in these services, making it more likely that they’ll click on links to files hosted on Google Docs, Microsoft  OneDrive or URLs that have been shortened. 

This is another way that bad actors introduce noise in their attacks, so detection systems have more checks to do. Systems must therefore look at the content beyond the link to identify its legitimacy. 

Multiple Attack Vectors, Channels And Devices/End-Points

Of course, companies spend a great deal of time, effort and money providing training to staff in an effort to forewarn them about the typical attack vectors and educate them on what red flags to look out for. However,  research shows that even highly computer-literate individuals were unable to successfully identify fake websites, even when expecting to see them!  

The best-produced fake site was able to fool more than 90% of participants, while the average was just under  40% of participants thinking that a fake site was genuine. This means that of every 10 employees in a company,  4 of them would be likely to take an action on a phishing website, compromising themselves and the company! 

The research highlights how 23% of participants looked only at the content of the website and did not use any other visual cues and it showed that the same percentage of participants did not have a good understanding of the padlock icon, with some thinking that it was more trustworthy when shown within the content area than in the browser header. 

Further, the research showed that “indicators that are designed to signal trustworthiness were not understood  (or even noticed) by many participants.” This highlights that using techniques to flag dangerous or high-risk content, may simply go unnoticed or even be ignored. 

So, ultimately, we see that trying to rely on the user as the defence barrier, especially when they are inside the castle gates, is not the best strategy. The goal must be to ensure that they never see a phishing email or website in the first place and provide training as a nice-to-do action.  

Any solution that can reduce an organization’s reliance on using their internal staff as amateur cyber security blockers will be a very attractive proposal.

Don’t have time to read the Success Story right now?


Visual-AI’s strength is its ability to see the world as humans see it, but at machine speed. Never tiring, never making mistakes and to a much higher accuracy than humans can achieve. Visual-AI, therefore, enables a new paradigm in combating phishing attacks where the attackers rely on:

  1. Building trust through the use of familiar and authoritative visual cues
  2. Evading detection by using graphical elements to replace machine-readable elements
  3. Evading detection by adding noise to, or eliminating/obfuscating, code


Examples of key visual elements exploited by bad actors that Visual-AI can detect:


Company Logo

Used in email, documents and websites, the logo of a bank, service or system can inspire confidence. It’s important to note that brands can have multiple versions of their logo and Bad actors will also use older or outdated logos in their attacks. Identifying the highest risk/most phished  brands in attacks quickly allows the PDS to take priority action on that message or site.

Impersonated Brands in Phishing Attacks
Examples of old brand logos often used in phishing attacks


Similar to logos, Scammers may use recognised and authoritative marks to increase trust. Safety marks, security cert marks, and padlock icons specifically in website content, etc. can be combined to ratchet up trust factors in recipients. The presence of these marks, in conjunction with other elements, can be a key indicator of a phishing attempt.

Examples of badges and certificate icons used in phishing attacks


The mini logo-based icon used in the tab of a website is another often-used technique to try and confuse victims. It can be another effective ranking factor.

Favicons are often exploited in phishing attacks

Examples of evasion techniques involving graphical elements that Visual-AI can detect:

Text Converted To Graphics

They will convert key ‘trigger’ words in the content to a graphic. For instance, words like ‘Username’, ‘Password’, ‘Login’, and ‘Credit Card Number’ will be converted from readable text to a JPG or PNG, but in such a way to be indistinguishable from the normal text to the user.

Sections Converted To Images

Rather than converting just a word at a time, they may convert an entire form to a graphic, overlaying the input fields above the graphic. In some cases, as outlined in the image to the right they will convert the entire email or site into a single graphic.

Bad actors will often convert an entire email or web page to a graphic to avoid detection

URLs Converted To Graphics

Similar to key ‘trigger’ words, a genuine URL is converted to a graphic, however, a link is then attached to the graphic that points to the fake site. In this way a user may see, but the link behind the image will point to

6 Common Detection-Evasion Techniques That Visual-AI Can Detect:

1. Keep The Lifespan Short

As outlined in a previous section, many systems rely on blacklists to identify threats. Bad actors will therefore run a site for a very short period before they change servers, IPs, domains etc., thereby avoiding being flagged. 2020 has also seen the rise of single-use URLs that therefore only last seconds!

2. Add Noise

A great way to evade is to confuse. They achieve this by adding significant ‘noise’ to the code of an email or site. They will change key attributes of graphics, like filenames and HTML attributes and metadata. They can even break up graphics into multiple small parts, as shown below. Using HTML or JavaScript these separate parts can be displayed seamlessly to a visitor in their web browser.

Example of a logo split into multiple parts to avoid phishing detection

3. Legitimise The Illegitimate

As well as being a fantastic method for tricking victims, bad actors will often use legitimate links in emails and websites, such as help, legal and even anti-fraud pages. They will also use a legitimate reply-to address, all of which helps to confuse the PDS.

Example of a fake phishing email using legitimate links

4. Nothing To See Here

As shown in the Apple example previously, bad actors will take the most sensitive section/s of content that can trigger a detection, or even the entire email/website and convert them into graphics. This greatly reduces or eliminates machine-readable content/code, giving the PDS nothing to work on.

5. Dynamic Content

They will use (often obfuscated) JavaScript to dynamically generate the contents of a page. This stops HTML parsers from being able to extract elements, such as form fields, for analysis because the page is only displayed when rendered by a web browser. This issue has been made worse by the introduction of WebAssembly, which provides many advantages for developers, but has been massively adopted by bad actors who are delighted with the fact that they can compile code, which makes it even easier to obfuscate.

6. Open The Windows

Bad actors can drive victims to genuine sites, but trigger an additional window, or a popup, to open above the legitimate login form in order to substitute their bad form, as shown in the examples below.

Example of fake popup windows used in phishing attacks

How Visual-AI Detects The ‘Undetectable’

Visual-AI sees all content with human eyes. It is something that the bad actors cannot avoid because their goal is to show humans an email, site or document that looks as natural and genuine as possible. By fully rendering the content (perhaps in a sandbox) and converting the output to an image, the PDS will have captured what a human will see. This can then be passed to VISUA’s Visual-AI engine for analysis.

Visual-AI In Anti-Phishing Step 1 - Render And Capture

1. Render & Capture

Render the web page/email and save it as a flat image for processing.

Visual-AI In Anti-Phishing Step 2 - Process The Image

2. Process The Image

Identify high-risk brands (for priority processing) and any anomalous attributes within the page/email.

Visual-AI In Anti-Phishing Step 3 - Visual Risk Scoring

3. Visual Risk Scoring

Calculate a risk score and pass it, with the identified anomalies, back to the master phishing detection system for final actions.


This whitepaper has highlighted the challenges faced by cyber-security companies and PDS providers in stopping phishing attacks. It shows the extreme and ingenious lengths bad actors go to in order to avoid detection and achieve their goals. And further outlined the role that Visual-AI has to play in enhancing detection and blocking of phishing attacks.

Humans To The Rescue?

We know that trained humans have the capabilities to detect phishing attacks to a very high degree of accuracy. The problem is the volumes they can process are very low. An army of humans would therefore be required to keep up with the volume of checks required. The work would also be tedious and tiring.

Artificial Intelligence To The Rescue?

AI addresses the issue of speed because it operates at ‘machine speed’; able to process millions of pieces of data in a fraction of the time it would take a human to do the same work. AI can also be trained to quickly and efficiently compare multiple strands of data and data-points to identify correlations that would otherwise be missed. The weakness of AI is that bad actors can relatively easily evade the employed data gathering techniques, which non-visual AI systems rely on, to produce their results.

Visual-AI + Standard AI Is The Answer!

By supplementing the current AI systems with advanced and targeted Visual-AI, you achieve a perfect combination that delivers ‘human’ visual analysis (but at machine speed) with AI data analysis.

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