AI Face Swap Attacks Explode 704%: How Deepfakes Threaten Identity Verification (and How to Stop Them)
Author: admin | 28 Feb 2024In This Post
Last month a report revealed the numbers showing a sharp rise in AI face swap online attacks that have also raised an eyebrow on the robustness of remote identity verification. In the second half of 2023, an increase of 704% in face swap injection attacks was witnessed compared to the first half. Generative AI has the capability of creating highly realistic images and videos where it can manipulate the key facial features to be used for spoofing the identity verification processes. Synthetic Identity fraud has increased due to AI face swaps that pose a serious risk to the digital identities of people.
Let’s find out how this synthesized media manipulative tactic of Face swaps works and what its impact on Identity Verification. We will also explore the key strategies and strategies to prevent face swap injection attacks highlighting the role of advanced facial recognition software in detecting face swap injection attacks.
Key Takeaways
- AI Face Swaps are malicious acts using advanced digital image manipulation. They swap real faces in images or videos.
- Deepfakes can bypass identity verification systems. They pose major online security threats like identity theft, fraud, and reputational damage.
- Video injection and media manipulation are the two main methods of swapping faces online.
- Beware of free face swap tools. They may collect your data and sell it to malicious actors.
- Facia is the world’s fastest face swap detection software that detects deepfake attacks in under one second.
What is AI Face Swap?
AI Face Swap is a digital image manipulation technique that is used to create Deep Fakes for malicious attacks. It allows fraudsters to create highly realistic videos and images of faces by swapping them digitally through Artificial Intelligence (AI). The AI algorithms analyze the facial features of the input facial data and swap it with the targeted face employing seamless procedures.
How Does AI Face Swap Work?
AI face swap online has impacted identity verification in threatening ways. Advancements in neural networks and machine learning algorithms lead to seamless and more realistic face swaps with time. Previously, face swap technology depended on image manipulation methods, including replacing one face with another using morphing algorithms. These techniques resulted in unnatural results and apparent distortions.
More advanced algorithms have been developed due to advancements in machine learning technology to create hyper-realistic face swaps. The major outburst in face swap technology is deep learning neural networks, also known as Convolutional Neural Networks (CNN), as they are trained on tremendous datasets of marked images, allowing them to understand and identify the minute details of user facial imprints. Advanced training enables the algorithm to recognize facial imprints, including the nose, eyes, and mouth position.
See below how the CNN network model works in creating face swaps:
| Facial Feature Detection |
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| Facial Feature Extraction |
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| Facial Feature Matching |
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| Blending |
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| Final Editing |
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| Output Image |
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Why Threat Actors are Opting for Face Swaps?
Face Swaps are now a favorite choice of identity fraud actors. It is because of the level of reality it reaches in creating a fake yet realistic digital facial identity. Another factor is the easy availability of Generative AI face swap online tools that are widely available to everyone. Tools such as Deepswap, Swapface, and Deepfacelive are the best choices for identity fraud attackers. Another risk highlighted in the report is the free versions of face swap applications that collect the user’s uploaded facial images and videos, manipulate them, and offer the data to malicious online platforms like the dark web itself. This calls for stronger online security protocols in remote identity verification systems using facial recognition.
Methods of Face Swaps
Mainly face swaps are carried out in two ways:
1. Video Injection Attacks that use code modification of data captured through virtual cameras
2. Photo Face Swaps is a sophisticated technique that employs a two-way approach in which splice attacks and video replay attacks work together to continuously swap faces and attempt to bypass facial recognition.
Facia’s Face Verification Secure Users and Thwarts Illegal Attempts
AI face swaps online used to be a useful tool for influencers and social media content creators as they are a source of entertainment and attract a massive audience. However, with the advancement in face swap technology, the threat of identity fraud has increased. Facia provides its face recognition services to secure users from unethical face-swapping attempts. Face recognition technology improves security by restricting illegal face swaps and ensures that the individual is a legitimate user. With the highest speed in the facial recognition industry worldwide, it offers speed and accuracy to prevent AI face swap attacks and ensures the security of the digital identities of users.
Are you still concerned about the increasing threat of identity theft and illegal face swaps? Secure your users with Facia’s face recognition technology!
To avail our services Contact Us Today!
Frequently Asked Questions
While often used interchangeably, FaceSwap and deepfakes do differ. FaceSwap is a simpler technique, employing basic tools to swap faces between images. Deepfakes, on the other hand, employ sophisticated AI algorithms like Generative Adversarial Networks (GANs) to create far more realistic face replacements, frequently seen in videos.
Face Swap Injection Attacks are a specific type of deepfake attack targeting remote identity verification systems. Attackers leverage deepfakes to bypass facial recognition by superimposing a target's face onto another person's body. This deception occurs during video calls or image captures, tricking the system into believing the attacker is the authorised user.
To identify AI-generated faces, be mindful of these red flags:
- Unnatural skin texture.
- Poorly synchronised lip movements.
- Inconsistencies in lighting or shadows.
- Lack of blinking or awkward eye movements.
- Unusual hair patterns.
- Blurred areas where the face meets the neck or hairline.
Yes, face swap attacks can be conducted during live video calls. Advanced software can process video streams in real-time to alter faces. For instance, a Hong Kong-based company lost nearly £26 million due to a video deepfake scam. Attackers impersonated executives and convinced an employee to make unauthorised transfers to fraudulent accounts.
Spotting a deepfake face swap hinges on observing subtle cues:
- Inconsistencies in facial geometry.
- Unnatural movements or expressions.
- Mismatches in lighting and skin tones.
- Audio discrepancies not aligning with visual mouth movements.
Businesses can fortify their defences with a multi-layered approach:
- Implement multi-factor authentication and biometric verification.
- Deploy advanced facial recognition with liveness detection capabilities.
- Train employees on the risks associated with deepfakes.
- Utilise AI detection tools to analyse video and audio for signs of manipulation.
- Establish clear protocols for verifying identities during sensitive communication.
