Akademik Veri Yönetim Sistemi
IEEE INTERNET COMPUTING, cilt.30, ss.1-10, 2026 (SCI-Expanded, Scopus)
Generative AI tools have increased the scale and accessibility of deceptive image creation, but the robustness of existing forgery detection methods under emerging editing tools and real-world social media conditions remains uncertain. This study evaluates state-of-the-art image forgery detectors on recent benchmarks and applies the strongest model to a historical influence-campaign case study: Russian troll activity during the 2016 U.S. presidential election. We find that TruFor is the most consistent detector across legacy and recent datasets, but that its performance degrades on edits produced by advanced tools such as Nano Banana. We then deploy a multimodal pipeline combining forgery localization, Optical Character Recognition (OCR), face recognition, and visual-language-model-based annotation to characterize manipulated images in the campaign. Our analysis suggests that Russian troll accounts relied primarily on low-effort meme-style edits and ridicule-oriented reputation attacks, disproportionately targeting prominent U.S. political figures including Hillary Clinton, Bill Clinton, and Barack Obama.