Detecting Text Manipulation in Images using Vision Language Models

Nov 1, 2025·
Vidit Vidit
,
Pavel Korshunov
,
Amir Mohammadi
,
Christophe Ecabert
,
Ketan Kotwal
Prof. Sébastien Marcel
Prof. Sébastien Marcel
· 0 min read
Abstract
Recent works have shown the effectiveness of Large Vision Language Models (VLMs or LVLMs) in image manipulation detection. However, text manipulation detection is largely missing in these studies. We bridge this knowledge gap by analyzing closedand open-source VLMs on different text manipulation datasets. Our results suggest that open-source models are getting closer, but still behind closed-source ones like GPT4o. Additionally, we benchmark image manipulation detection-specific VLMs for text manipulation detection and show that they suffer from the generalization problem. We benchmark VLMs for manipulations done on in-the-wild scene texts and on fantasy ID cards, where the latter mimic a challenging real-world misuse.
Type
Publication
British Machine Vision Conference
publications
Text Manipulation Detection Deepfakes
Prof. Sébastien Marcel
Authors
Prof. Sébastien Marcel (he)
Senior Research Scientist
Prof Sébastien Marcel (IEEE Fellow and IAPR Fellow) is a senior research scientist at the Idiap Research Institute (Switzerland), he heads the Biometrics Security and Privacy group and conducts research on face recognition, speaker recognition, vein recognition, attack detection (presentation attacks, morphing attacks, deepfakes) and template protection. He is also Professor at the University de Lausanne (UNIL) at the School of Criminal Justice. He is also the Director of the Swiss Center for Biometrics at Idiap, which conducts certifications of biometric products. He was Associate Editor and Guest Editor of IEEE journals (TBIOM, SPL, TIFS and SPM). He is also the lead Editor of the Springer Handbook of Biometrics Anti-Spoofing (Editions 1, 2 and 3). Since June 2025 he is a member of the Idiap Direction ad interim.