IndicSideFace: A Dataset for Advancing Deepfake Detection on Side-Face Perspectives of Indian Subjects

Abstract

The rapid advancement of generative models and their misuse have made deepfake detection a crucial area of research. However, existing datasets and detection techniques predominantly focus on frontal-face perspectives, leaving sideface views largely underexplored. To bridge this gap, we present IndicSideFace, a novel dataset specifically curated for advancing deepfake detection on side-face perspectives of Indian subjects. This dataset encompasses a diverse range of side-face angles, varying lighting conditions, and demographic attributes, providing a comprehensive benchmark for evaluating detection algorithms. Our experiments using state-of-the-art models highlight the unique challenges posed by side-face deepfakes, such as partial facial feature visibility and uncommon head poses. The findings reveal significant limitations in existing detection approaches when applied to side-face perspectives, underscoring the need for specialized solutions. With IndicSideFace, we aim to strengthen the resilience of deepfake detectors and stimulate further research in this critical yet underexplored domain. © 2025 Elsevier B.V., All rights reserved.