Keynote Speakers

Facial image quality assessment plays a crucial role in biometric authentication throughout all areas of application. Ensuring the reliability and accuracy of facial images is paramount for these applications to function effectively. By evaluating images quality components, such as pose or focus, we can enhance the performance of facial image recognition systems and minimise the risk of errors.

Additionally, in a world, where biometric systems interact with each other more and more, interoperability of biometric algorithms becomes increasingly important, making a vendor-independent approach indispensable. Here, OFIQ – the Open-Source Face Image Quality framework – brings a transparent, open source approach for this important task into play.

In the rapidly evolving landscape of technology, the convergence of digital realms such as nanotechnology, biotechnology, internet of things, robotics and AI presents both unprecedented opportunities and formidable challenges. This talk aims to delve into the intricate interplay of information technology (Bits), material science (Atoms), neural networks (Neurons), and genetic engineering (Genes). We will explore how this synergistic blend is driving a revolution, reshaping industries, and redefining what it means to be human in the digital age.

As we stand at the crossroads of this technological convergence, our discussion will pivot around critical themes such as trust and security in a digitized world, and the field of digital forensics. How can we build trust in systems that merge human intelligence with artificial intelligence? What are the implications for security when our biological data becomes part of the digital framework? And how does digital forensic investigation evolve as these disparate fields merge?

Join us in this thought-provoking session to unravel the complexities of the convergence revolution. We will not only address the challenges but also highlight the opportunities that lie ahead. Whether you are a tech enthusiast, a professional in the field, or simply intrigued by the future of technology, this talk promises to offer valuable insights and stimulate a rich conversation about our shared digital and physical future.

Synthetic voice generated by AI, or voice deepfakes, are a serious threat to the security and privacy of people and organizations. Voice spoofing and presentation attacks can impersonate, trick, or influence others, with possibly severe outcomes. Over the last decade, the community has made great progress in detecting synthetic voices, with reported true positive and true negative rates as high as 99%.

However, in recent years some research began to question the generalization capability of state-of-the-art spoofing detectors, in particular with respect to perturbations due to real world or artificial signal manipulation. We have expanded on these early notions by arguing that more realistic datasets, capturing characteristics of real-world use of spoofing technologies, are integral towards developing general and robust methods.

In this presentation we will demonstrate with practical examples that this is the case, and that the high accuracy figures reported may give a false sense of protection against audio deepfakes. We will show how the current methodologies on audio deepfake detection research may result in excellent performance on research benchmarks but can completely fail in the wild, and how an improved methodology can help achieve more trustworthy systems in real-life situations.