Identity theft is a serious issue worldwide. Every year, countless individuals and organizations fall victim to identity fraud. One thing’s clear. As criminals use more sophisticated techniques and tactics to steal users’ identities, businesses must set up advanced ID verification processes to safeguard their records. This means implementing both document and selfie checks for a dual and more robust approach to Know Your Customer (KYC) compliance.
In practice, this means using modern identity verification systems that often incorporate document authentication into their fraud prevention strategy. These systems 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 it is and why it’s important to build a proper digital onboarding process that detects deepfakes, masks, forged documents, and other forms of manipulation.
What is Image Manipulation?
Image manipulation, also referred to as 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 and fraudulent activities.
That’s why the same manipulation technique is widely used to create magazine covers or photo albums. Though it is skillful artwork, the method has also been used to fraudulently claim and deceive the public. Over the past few years, bad actors have started using image manipulation to carry out financial crimes and malicious attacks, spread disinformation, and, in this way, use someone else’s identity.
Why Do People Use Image Manipulation for Identity Verification?
(An example of 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. Consequently, identity verification systems are designed to detect and prevent such manipulations, ensuring the accuracy and security of the verification process.
A few common examples of why a person might want to fake their personal information through image manipulation 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
(An example of a manipulated profile photo on an 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.
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.
Image manipulation helps us achieve artistic effects. Still, some people misuse this technique for deceptive purposes, especially in identity verification. Like any technology, image manipulation has been used for both the good and the bad of our imaginations.
However, this 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 using different technologies.
Here are some of the most used advancements that help businesses detect image manipulation:
1. Computer Vision Filters
(An example of an altered ID document after using a special computer vision filter,
faked parts of the document are brighter)
(An opposite example of a real identity document after using a 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.
2. CNN & AI
(An example of an Image forgery using the Copy-Move method)
Cybercriminals are using advanced image processing software like GNU Gimp and Adobe Photoshop to doctor images and create fake ID documents.
Thankfully, there’s a way to verify the authenticity of such images. For example, back 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:
- Content-aware fill and patch match (for copy and paste)
- Content-aware healing (for copy/paste and splicing)
- Clone-stamp (for copy/pasting)
- Seam carving (for image retargeting)
- Inpainting (for the reconstruction of damaged parts)
- Alpha Matting (for merging)
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 the potential of CNN in detecting image forgery. CNN has features similar to those of 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, and he 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.
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.
At the same time, AI, or Artificial intelligence, 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.
Final Thoughts
Some tools already exist that can help you trace digital manipulation of photos. Adobe itself is developing 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 6th of August, 2024, to reflect the latest insights.
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