Detecting Localized Deepfakes: How Well Do Synthetic Image Detectors Handle Inpainting?

• URL: http://arxiv.org/abs/2512.16688v1
• Date: Thu, 18 Dec 2025 15:54:51 GMT
• Title: Detecting Localized Deepfakes: How Well Do Synthetic Image Detectors Handle Inpainting?
• Authors: Serafino Pandolfini, Lorenzo Pellegrini, Matteo Ferrara, Davide Maltoni,
• Abstract summary: generative AI has enabled highly realistic image manipulations, including inpainting and region-level editing.<n>These approaches preserve most of the original visual context and are increasingly exploited in cybersecurity-relevant threat scenarios.<n>This work presents a systematic evaluation of state-of-the-art detectors, originally trained for the deepfake detection on fully synthetic images, when applied to a distinct challenge: localized inpainting detection.
• Score: 2.6743542260081408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of generative AI has enabled highly realistic image manipulations, including inpainting and region-level editing. These approaches preserve most of the original visual context and are increasingly exploited in cybersecurity-relevant threat scenarios. While numerous detectors have been proposed for identifying fully synthetic images, their ability to generalize to localized manipulations remains insufficiently characterized. This work presents a systematic evaluation of state-of-the-art detectors, originally trained for the deepfake detection on fully synthetic images, when applied to a distinct challenge: localized inpainting detection. The study leverages multiple datasets spanning diverse generators, mask sizes, and inpainting techniques. Our experiments show that models trained on a large set of generators exhibit partial transferability to inpainting-based edits and can reliably detect medium- and large-area manipulations or regeneration-style inpainting, outperforming many existing ad hoc detection approaches.

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