Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach

• URL: http://arxiv.org/abs/2511.00352v1
• Date: Sat, 01 Nov 2025 01:35:54 GMT
• Title: Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach
• Authors: Mohd Ruhul Ameen, Akif Islam,
• Abstract summary: Traditional deepfake detection methods fail against text-to-image systems such as Stable Diffusion and DALL-E.<n>This paper introduces a diffusion-based forensic framework that leverages multi-strength image reconstruction dynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid rise of generative diffusion models has made distinguishing authentic visual content from synthetic imagery increasingly challenging. Traditional deepfake detection methods, which rely on frequency or pixel-level artifacts, fail against modern text-to-image systems such as Stable Diffusion and DALL-E that produce photorealistic and artifact-free results. This paper introduces a diffusion-based forensic framework that leverages multi-strength image reconstruction dynamics, termed diffusion snap-back, to identify AI-generated images. By analysing how reconstruction metrics (LPIPS, SSIM, and PSNR) evolve across varying noise strengths, we extract interpretable manifold-based features that differentiate real and synthetic images. Evaluated on a balanced dataset of 4,000 images, our approach achieves 0.993 AUROC under cross-validation and remains robust to common distortions such as compression and noise. Despite using limited data and a single diffusion backbone (Stable Diffusion v1.5), the proposed method demonstrates strong generalization and interpretability, offering a foundation for scalable, model-agnostic synthetic media forensics.

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