Can Generative Models Actually Forge Realistic Identity Documents?

Alexander Vinogradov

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arXiv · 2601.00829

Can Generative Models Actually Forge Realistic Identity Documents?

Alexander Vinogradov

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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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Alexander Vinogradov. 2025-12-25. Can Generative Models Actually Forge Realistic Identity Documents?. https://arxiv.org/abs/2601.00829

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