Can Generative Models Actually Forge Realistic Identity Documents?

• URL: http://arxiv.org/abs/2601.00829v1
• Date: Thu, 25 Dec 2025 00:56:50 GMT
• Title: Can Generative Models Actually Forge Realistic Identity Documents?
• Abstract summary: Open-source and publicly accessible generative models can produce identity document forgeries.<n>Risk of generative identity document deepfakes achieving forensic-level authenticity may be overestimated.
• Score: 51.56484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative image models have recently shown significant progress in image realism, leading to public concerns about their potential misuse for document forgery. This paper explores whether contemporary open-source and publicly accessible diffusion-based generative models can produce identity document forgeries that could realistically bypass human or automated verification systems. We evaluate text-to-image and image-to-image generation pipelines using multiple publicly available generative model families, including Stable Diffusion, Qwen, Flux, Nano-Banana, and others. The findings indicate that while current generative models can simulate surface-level document aesthetics, they fail to reproduce structural and forensic authenticity. Consequently, the risk of generative identity document deepfakes achieving forensic-level authenticity may be overestimated, underscoring the value of collaboration between machine learning practitioners and document-forensics experts in realistic risk assessment.

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