Outdoor Degraded Spliced Video Dataset (ODSVD): Collection, Annotation, Forensic Challenges, and Baselines Towards Strengthen Objective Measures of Evidence

Objective measures of evidence are critical in digital video forensics for establishing the authenticity and integrity of multimedia content. However, the development and benchmarking of such measures are hindered by the lack of comprehensive, real-world datasets that reflect the complexities of outdoor environments and splicing-based tampering. Consequently, video forensics approaches for detecting video tampering have received much attention in the forensics field in terms of crime scene investigations and are also presented as courtroom evidence. However, research on video-based splicing detection in outdoor scenes is lacking. In this paper, we introduce a new large-scale annotated spliced video dataset entitled “Outdoor Degraded Spliced Video Dataset (ODSVD)” that provides real-world outdoor-scenes under challenging atmospheric conditions. The proposed dataset contains spliced and corresponding authentic video clips of real-world scenes in atmospheric conditions, i.e., Fog, Rain, Clear Day, Clear Night, Night + Rain, Night + Fog, and Rain + Wind conditions. Along with these conditions, other challenging conditions, including acentric distribution of spliced objects, inter/intra class variability of spliced objects, and metadata tag-independent forged scenes, are also present in the dataset. The dataset contains 300 spliced and 300 corresponding original video clips in different atmospheric conditions. Additionally, 100 spliced video clips compressed using various social network sites (YouTube, Facebook, and Instagram) are included in the ODSVD dataset. In total, the dataset contains approximately 11.34 million frames (2700 to 4500 frames per video) in combination with the spliced and original clips. To make the dataset more efficient, the ground truth of spliced objects in the extracted frames is defined in the form of bounding boxes and binary masks. Using the ODSVD dataset, a quantitative comparison of the state-of-the-art splicing detection methods is performed. Unsurprisingly, the study reveals that the existing splicing detection techniques perform poorly when challenging atmospheric conditions are present, and there is still considerable scope for further improvement.