When 2D Cues Fail: Improving Image Manipulation Localization with Reliable 3D Geometry

2026-07-20

AI Summary

Existing image manipulation localization methods rely primarily on 2D cues and suffer significant performance degradation when tampered regions are seamlessly blended with the background. This work proposes a geometry-aware localization framework that, for the first time, incorporates 3D geometric cues—such as depth and surface normals derived from monocular reconstruction—into the task. By assessing the reliability of these 3D cues, the method employs a multi-scale fusion mechanism to selectively integrate them with RGB features. The proposed approach achieves notably improved localization accuracy with minimal additional computational overhead, demonstrating that trustworthy 3D geometric information effectively complements conventional 2D forensic cues.

Abstract

Existing image manipulation localization (IML) methods rely heavily on 2D forensic cues, such as low-level artifacts, noise traces, and semantic inconsistencies in the manipulated image. While effective in many cases, these cues become much less discriminative when manipulated regions are well blended with their surrounding context in appearance. In such cases, a manipulated region may remain locally appearance-consistent, but still violate the geometric structure of the surrounding scene. This limitation motivates us to go beyond purely 2D evidence and introduce geometric reasoning into IML. To this end, we leverage monocular reconstruction to obtain auxiliary geometric cues, including depth and surface normals. However, a key challenge lies in the fact that reconstructed geometry on manipulated images is inherently noisy and cannot be used naively. Rather than treating depth and normals as direct evidence, we estimate their reliability and exploit them selectively for localization. Based on this principle, we design a geometry-aware framework (GFrame) that fuses reliable geometric cues with RGB features and propagates them across scales to improve fine-grained localization. Extensive experiments show that the proposed method achieves excellent performance under limited budget constraints. These results indicate that reliable 3D geometry provides complementary forensic evidence beyond traditional 2D cues for IML. Related code will be released.

Problem

• image manipulation localization
• 2D forensic cues
• geometric inconsistency
• appearance-consistent manipulation
• 3D geometry

Innovation

• image manipulation localization
• 3D geometry
• monocular reconstruction
• reliability estimation
• geometry-aware framework