Image Manipulation in Identity Verification

Image manipulation in identity verification has become a significant concern for businesses across the globe. In this post, we will find out what exactly is image manipulation, and how technologies like AI, CNN, and Special CV filters can help us detect this manipulation.

Image Manipulation in Identity Verification-cover

Identity theft is a serious issue all over the world. Every year, countless individuals and organizations fall victim to identity fraud. As cyber attackers are using different tactics to steal personal identity, organizations must set up advanced ID verification solutions to safeguard their records.

However, modern identity verification systems often incorporate document authentication as part of their fraud prevention strategy. They verify the legitimacy of government-issued IDs, such as driver’s licenses or passports, by checking security features like holograms, watermarks, and microprinting.

Yet, image manipulation in identity verification remains a big headache for many businesses. Let’s find out what exactly it is.

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What is Image Manipulation?

Image manipulation or photo manipulation is a process that involves altering or transforming a digital image using different techniques to achieve desired results. This can include tasks such as retouching, resizing, adding or removing elements, adjusting colors, or applying artistic effects to change the visual appearance of a picture. Image manipulation can be done for creative purposes, photo editing, or, in some cases, for deceptive or fraudulent activities.

That’s why the same manipulation technique is widely used to create magazine covers or photo albums. Though it is a skillful artwork, the method has also been used for fraudulent claims and to deceive the public. Over the past few years, bad actors have started using image manipulation to carry out financial crimes, malicious attacks, spread disinformation, and, in this way, use someone else’s identity.

Why Do Fraudsters Use Image Manipulation?

Fraudster using image editing software for forging IDs
(ID card forgery using image editing software)

In the context of identity verification, image manipulation refers to the fraudulent alteration or tampering of images or documents presented during the verification process. This can involve the use of software tools or techniques to doctor images, documents, or biometric data in an attempt to deceive the verification system and gain unauthorized access or approval. Identity verification systems are designed to detect and prevent such manipulations to ensure the accuracy and security of the verification process.

A few common ones are listed below:

To Create Fake ID Documents

In the majority of cases, cybercriminals use image manipulation techniques to create fake IDs, which can be used to gain access to a system or service illegally.

Fake ID documents are identical to the original ones and carry a doctored photo of the authorized user. Fake IDs have been excessively used for identification.

Making fake IDs has become quite easy — thanks to the easy availability of low-cost, high-resolution printers and photo-editing software.

To Gain Access to Personally Identifiable Information

A fraudster can manipulate your image to gain access to your personally identifiable information (PII), such as name, address, social security number, license number, bank details, and more.

To Breach Biometric Face Recognition Systems

Example of a forged ID with a fake face
(Faked face photo on ID card)

Scam artists can also manipulate images to access the facial recognition system. It is becoming a common scenario in corporate identity theft. Fraudsters are using 3D masks and printed photos to bypass biometric face authentication systems.

Therefore, companies need state-of-the-art identity verification solutions that can verify government-issued identity documents in real-time and catch fake IDs.

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How Can Image Manipulation Be Detected?

Undoubtedly, technology is making the world a better place to live. However, the technologies we use to make positive changes can also be turned against us, and image manipulation is one of its examples.

The image manipulation technique helps us achieve artistic effects. Still, on the flip side, some people misuse this technique for deceptive purposes, especially in identity verification.

So, like any technology, image manipulation has been used for both the best and the worst of our imaginations. However, it does not mean we can’t prevent the misuse of image manipulation in identity verification. Numerous identity verification providers are coming forward to help individuals, and businesses detect image manipulation with the help of different technologies.

Let’s get to know about those technologies one by one:

Detecting Image Manipulation Using Computer Vision

Fake ID under a special filter uncovering the parts that have been tampered with (they're brighter)
(Faked identity document after using special computer vision filter,
faked parts of the document are brighter)

Real ID under a special filter showing no signs of tampering
(Real identity document after using special computer vision filter)

Computer vision special filters are one of the effective techniques to identify the manipulation of images. It can prove a handy method when it comes to identity verification. This forensic technique lets you know if the image is digitally modified or not.

Spotting Image Manipulation Using CNN & AI

Two images of lion prides side by side. The right one has been tampered with by using copy-move method
(Image forgery using Copy-Move method)

Cybercriminals are using advanced image processing software like GNU Gimp and Adobe Photoshop to doctor images and create fake ID documents.

Fortunately, there is a way to verify the authenticity of such images. In 2013, the IEEE Information Forensics and Security Technical Committee launched the first forensic challenge to address this problem.

The committee produced an open dataset of digital images taken under various lighting conditions. Then, they manipulated images using algorithms that included:

Many people were asked to participate in this challenge and identify images as faked or never manipulated. Some of the participants were able to detect forged areas in pictures using their human visual cortex. Based on the results, researchers saw potential in CNN to detect image forgery. CNN has features similar to our visual cortex.

CNN, or Convolution neural network, is a class of machine learning that helps in analyzing visual imagery. Many giant tech companies are already using CNN for image classification and recognition because of its high accuracy.

Yann LeCun designed CNN in the late 90s, who was a computer scientist. CNN implements a hierarchical paradigm that works on building a network and provides fully connected layers where all neurons are linked to each other.

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Some of the famous examples of CNN in image classification include:

  • Automatic tagging on Facebook
  • Product recommendations on Amazon
  • Google also uses it to search for users’ photos.

As fraudsters are using advanced technology to carry out image manipulation in identity verification, you can’t rely on traditional techniques to cope with this challenge.

AI or Artificial intelligence (AI) can be beneficial in detecting image manipulation. Researchers across the world are developing various forensic tools with the help of AI to determine image manipulation by examining the noise distribution, lighting, and pixel value of a photo.

Already, there are some tools out there that can help you trace digital manipulation of photos. Adobe itself is finding new capabilities in artificial intelligence to detect image manipulation. Though AI-enabled tools are in the initial stage, they have great potential to provide a deep understanding of a photo’s authenticity in the coming years.

If you want to implement an easy-to-use yet advanced identity verification system within your organization, you can rely on iDenfy. We can help you with face recognition, liveness detection, and identity document check, contact us.

This blog post was updated on the 27th of October, 2023, to reflect the latest insights.
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