Recent advancements in deep learning algorithms, such as Generative Adversarial Networks (GANs) and Diffusion models, have led to a surge in the creation of highly realistic images and videos, which are often indistinguishable from genuine content to the human eye. While these developments have benefited the entertainment industry, they are often used to spread misinformation and manipulate public opinion. Moreover, in the security domain, synthetic and manipulated images and videos are frequently utilized to impersonate individuals, enabling unauthorized parties to bypass systems for biometric authentication gaining illegal access to sensitive information. Accurate detection of fake data is crucial for preventing security breaches and safeguarding the integrity of security measures and public information.



The proposed workshop aims to present the latest developments in the creation, evaluation, impact, and countermeasures against visual attacks on biometric systems. We invite practitioners, researchers and engineers from biometrics, signal processing, material science, mathematics, computer vision and machine learning to contribute their expertise to underpin the highlighted challenges.



Topics of interest include, but are not limited to:

- Human-oriented generative models and image/video synthesis techniques,
- Detection of tempered, manipulated and synthetic content,
- Deepfake generation and detection,
- Morphing attack generation and detection,
- Image manipulation attacks in biometrics verification and identification scenarios,
- Video manipulation attacks,
- Physical (presentation) attacks on biometric systems,
- Forensic analysis of behavioral biometrics,
- Ethical and societal implications of manipulation techniques,
- Synthetic realities,
- Relevant case studies and applications,
- New datasets and performance benchmarks,
- Watermarking.