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"FoodFake-30K: A Dataset for Real vs. AI-Generated Food Image Detection"

Dhruval Patel

IEEE DataPort, 2026

Abstract

"FoodFake-30K is a large-scale benchmark dataset for AI-generated food image detection, containing 30,000 images across 15 diverse food categories. The dataset includes 15,000 real food photographs sourced from Pexels, Unsplash, Pixabay, Reddit (r\/food), and Food-101, alongside 15,000 AI-generated images produced by two state-of-the-art generative models: Flux-2 Klein (9B-parameter Diffusion Transformer, FP8) and Z-Image Turbo (AuraFlow Diffusion Transformer, BF16).Real images underwent a rigorous preprocessing pipeline \u2014 CLIP-based semantic filtering (openai\/clip-vit-large-patch14), perceptual hash deduplication (pHash), and manual review. AI image prompts were generated using Qwen 2.5 Coder 14B with structured diversity slots (angle \u00d7 lighting \u00d7 scene \u00d7 lens) to maximize visual variety. Images are provided at 1024\u00d71024 resolution (27,400 images) and 512\u00d7512 resolution (2,600 Food-101 images). The 15 food categories span global cuisines: baklava, biryani, burger, cake\/pastry, croissant, dim sum, falafel, hummus, pizza, plov, ramen, salad, steak, sushi, and tacos \u2014 with 2,000 images per category (1,000 real + 500 Flux + 500 Z-Turbo). FoodFake-30K supports research in fake image detection, image forensics, food authenticity verification, deepfake detection benchmarking, and cross-category generalization studies. All metadata including generation prompts, seeds, and source URLs is provided under CC BY 4.0."

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