Exciting News!! Full Paper Submission Extended to July 10, 2024, 23:59:59 PDT

The vulnerabilities of face recognition algorithms are limitless; hence this special session covers a wide range of topics that highlight the positive and negative aspects of the factors affecting face recognition. The topics include deepfake, the use of synthetic media for privacy-preserving learning, facial attribute annonymization, adversarial attacks, morphing, and presentation attacks.

Face recognition has been proven one of the most effective for establishing identities; however, the malicious purposes of intruders and the advancement of automated technologies have led to the development of several anomalies that can trick the system. However, the literature rarely describes these different anomalies under one roof, which limits the understanding of the functioning of the different anomalies or features that might not be an adversary but are used as an adversary due to poor network learning. This session aims to provide a comprehensive understanding of the success of face recognition algorithms and how different factors contribute to their success such as synthetic images or failure such as adversarial attacks. We assert due to the involvement of the significant inter-disciplinary concept, the proposal can help in understanding face recognition from a top level. For example, the generation of deepfake and adversarial attacks is significantly different but in the end, they are manipulating the deep-level features of deep face recognition. Understanding how these factors are working can help us in developing a universally robust deep face recognition. The proposed special session is critical and highly relevant to the audience of the main conference; therefore, we request the community to actively take part and submit their high-quality papers to understand and protect the integrity of deep face recognition networks.