Trusted Proof: Verifying Photographic Authenticity via Multi-Camera Stereo Vision for Insurance and Forensic Applications

The rise of generative AI models such as Midjourney, DALL•E, and Stable Diffusion has made it increasingly difficult to distinguish synthetic images from genuine photographs, posing serious risks to domains reliant on visual evidence, including insurance, journalism, and forensics. We propose a practical solution: using the stereo vision capabilities of modern smartphones (e.g., iPhone Pro, Samsung Galaxy) to verify image authenticity through depth reconstruction, focal length estimation, and geometric consistency. Embedded metadata such as GPS coordinates, timestamps, device model, and optional motion sensor data are also collected to assist validation. By capturing multiple views and analyzing 3D structure alongside intrinsic parameters and contextual metadata, our method identifies physical cues that AI-generated images cannot convincingly replicate. This lightweight, hardware-anchored approach can be deployed directly on consumer devices or integrated into secure mobile workflows. Experimental results show reliable detection of synthetic content, with future work focusing on broader device coverage and blockchain-based provenance for end-to-end assurance.