What is Face Morphing?
Face morphing is a sophisticated image manipulation technique where facial features from two or more resource images are warped to generate a new identity. There are two possibilities, either the newly generated identity is the combination of resource images revealing extreme resemblance or a whole non-existent identity to dodge ID verification systems.
Facial morphing attacks use advanced algorithms, sophisticated machine learning tools, and software to produce sharp results. Morphing attacks present distressing challenges in the modern fast-paced technological world, harming national security and tormenting trust in authentication systems. It’s arduous to differentiate between genuine identities and spoofed attempts, damaging trust in digital platforms, as it’s daunting to figure out whom to trust or whom to not.
Quick Insights into How Face Morphing Works?
Facial morphing technology leverages advanced Generative Adversarial Networks (GANs), advanced algorithms, computer vision, and 3D modeling techniques to generate highly realistic and sharp output. Face morphing technology works in a series of steps to create extremely realistic identities that even bypass biometric authentication systems.
Image Acquisition & Face Detection
Imposters spend a lot of time collecting images from multiple sources such as digital footprints or manipulatively acquiring sensitive to get access to the required data. Once the images of the targeted individuals are collected, advanced algorithms and machine learning tools to convert the images as per requirements. The facial features or key points such as the shape of the nose, depth of the mouth, the distance between two eyes, or the contour of jawlines, from the resource images and live videos are detected.
Features Extraction & Correspondence
The required features from the source images are extracted. Advanced morphing attacks also involve the extraction of in-depth features such as skin texture, facial hair, or wrinkles to make the spoofed attempts highly realistic. After feature extraction, the correspondence points from both images are recognized, to merge the points efficaciously. Advanced algorithms are leveraged to match the facial features on the basis of facial geometry or spatial relationships, to give sharp results with no errors.
Image Deformation & Warping
This step is very crucial in facial morphing attacks, as facial features from the resourced images are deformed and blended into each other using mesh-based techniques, presenting smooth and extremely realistic results. In deformation, the facial features of the fed images are deformed, however, the overall geometry of the faces is retained to generate natural images. The pixels from both images are also blended and warped into each other to drive a smooth transition.
Blending & Interpolation
The sourced images are blended into each other to generate a single identity, which resembles the fed images. Cross-dissolve or alpha-blending facial morphing techniques are employed to accurately merge the images and produce highly realistic identities. Minor details like skin color and texture are also minutely warped to give sharp output. This step is performed smoothly to produce an extremely realistic facial image and reduce the chances of discrepancy.
Refinement & Output Generation
This is the last stage in facial morphing attacks, where the interpolated image is refined to enhance the quality of the morphed face. The overall appearance of the morphed identity is enhanced and a single cohesive face is generated which contains elements from both of the source images. Facial morphed images are used for various purposes which could be positive or negative use cases. However, concerns are provoked about the exploitation of facial morphing technology and its unethical uses for illicit activities or unauthorized access to the systems.
Preventive Strategies to Counteract Morphing Attacks
To counteract the far-reaching consequences and threats associated with facial morphing attacks, individuals, as well as organizations, are required to stay cautious and keep updated with the latest trends in facial morphing techniques. ID verification solutions bear the major responsibility to implement effective detection and prevention strategies to stay ahead of the latest facial morphing attacks.
Biometric Liveness Detection
Biometric liveness detection a sophisticated facial recognition technology authenticates whether the claimed identity is live or not. This approach is preferred to detect presentation attacks and is effective in preventing spoofed attempts. IDV solutions must integrate liveness detection to accurately authenticate individuals and ward off spoofed attacks.
Texture Analysis
Advanced image recognition tools are trained on a large volume of databases to accurately detect anomalies and unusual patterns. Facial recognition technology must integrate texture analysis algorithms to minutely monitor the texture and color of skin to precisely verify identities.
3D Facial Recognition
3D facial recognition technology analyzes the three-dimensional aspects of facial attributes and interprets in-depth information, which makes it a reliable and secure way to detect morphed attacks. 3D facial recognition integrated with biometric liveness detection could offer promising applications in accurate ID verification and spoofed attack detection, safeguarding national security.
Wrap-Up
Facial morphing technology which was initially developed to present remarkable benefits in entertainment & media production is now used for malicious purposes to attain unauthorized access to systems and networks. With the distressing rate at which malicious actors are employing facial morphing attacks to conduct illicit activities in the disguise of others, it’s crucial to stay cautious and implement robust facial recognition technology integrated with liveness detection and texture analysis to actively detect spoofed attacks.
