The generation of synthetic images has made a strong entrance in various sectors, including biometric verification. Its potential to enhance recognition models coexists with emerging risks such as spoofing attacks and deepfakes. The key lies in balancing innovation, security, and ethics.
By Ángela Sánchez Pérez, R&D AI Researcher
Synthetic data is gaining ground in several areas of society—such as healthcare, finance, and education—where it is already used to train models, optimize processes, or strengthen security. At the same time, the use of tools capable of generating images from other images or from text instructions (prompts) is becoming increasingly common. A representative example is the use of models like ChatGPT, which can create synthetic images with specific features, such as style changes or completely new compositions.
However, these tools also involve certain risks, including the potential to facilitate attacks against identity verification systems during digital onboarding processes.
Synthetic data in facial recognition: quality or consistency?
While generating high-quality synthetic data is now within reach for many, its usefulness depends on context. In facial recognition, it is essential that synthetic individuals have a sufficient number of images that reflect the variability typical of real-world images: changes in pose, lighting, facial expressions, occlusions, and more.
Additionally, it is crucial that the images consistently preserve the identity of the subject without blending features from different people. In this regard, identity consistency becomes a priority—sometimes even more important than visual quality.
Preserving identity: a critical challenge in synthetic biometrics
This aspect, known as identity preservation, represents one of the main challenges in generating synthetic data for biometric applications. If the images fail to accurately maintain the distinctive traits of a person—or if they unintentionally combine characteristics from different individuals—their usefulness for training or evaluating facial recognition systems becomes compromised.
As a result, the controlled generation of synthetic identities remains a central focus of ongoing research.
More ethical and robust training with synthetic data
Synthetic data generation also offers great benefits during the model training phase, especially when used to complement real data. In this context, synthetic data can help reduce both error and bias in training datasets by incorporating greater diversity of conditions and offering a more balanced representation.
Moreover, it reduces dependence on real data, which is often sensitive in facial recognition applications. Minimizing the need to collect and process personal information not only strengthens individual privacy but also facilitates compliance with ethical and legal regulations related to data protection.
Deepfakes and spoofing attacks: growing threats
The increasingly widespread access to synthetic data generation tools presents significant challenges for biometric security. Facial recognition systems face a growing risk of vulnerability to techniques such as deepfakes and other visual spoofing methods. These technologies enable the creation of highly realistic images or videos capable of deceiving authentication mechanisms, compromising the integrity of identity verification processes.
Strengthening security against artificially generated identities
In the face of these emerging threats, both academia and the private sector are intensifying efforts to develop advanced biometric security systems. The goal is to enhance the detection and mitigation of spoofing attempts using artificially generated content, ensuring user protection and the reliability of verification processes.
Social and ethical Implications of synthetic identities
Beyond technical aspects, the proliferation of synthetic images raises significant social and ethical concerns. The ability to manipulate or create highly realistic identities opens the door to new forms of misinformation, digital fraud, and identity theft.
This phenomenon jeopardizes trust in visual evidence—in both legal and everyday contexts—and raises questions about accountability in the creation and dissemination of such content.
In response to this, at Facephi we are committed to developing technologies that not only ensure technical effectiveness but also uphold privacy, integrity, and the protection of digital identity. With a focus on responsible innovation, we contribute to building a safer and more trustworthy digital environment for everyone.
Discover how our solutions are driving ethical and secure digital identity management, tailored to the challenges of an increasingly digital world.