Abstract

Trust in visual evidence increasingly depends on reliable image forgery detection and localization. Although evaluation datasets and metrics are becoming standardized, training practices remain fragmented—often relying on unreleased corpora and narrow artifact cues that hinder reproducibility and cross-domain transfer. This dissertation addresses these gaps through integrated frameworks combining model design, data standardization, synthetic data generation, validation, and generalization across manipulation types and applications.

Date

2025

Authors

Soumyaroop Nandi

Institution

University of Southern California