ArticleScientific reports2025
Examining the role of compression in influencing AI-generated image authenticity.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Re: Most et al: Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation.Ophthalmology science · 2026Article
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4 authors.
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Abstract
The rapid development of AI-generated Content (AIGC) in recent years has narrowed the gap between virtual and realistic. Among them, AI-generated Images (AIGIs) are particularly significant, as their emergence has led to a profound impact on education, art, virtual reality, etc. However, little research has been conducted to investigate whether compression artifacts can influence the subjective authenticity of AIGIs. In this paper, we systematically study this problem by creating the first-ever AIGC image dataset for subjective evaluations of authenticity discrimination. The dataset contains 500 AIGIs and 500 natural images with a resolution of 768 × 768. The content of the images therein has been categorized into 5 major categories and 20 subcategories to study the performance of AIGIs on different contents. Subsequently, we introduce four varying degrees of compression distortion (QP = 22, 32, 42, 52) on all images utilizing the standard Versatile Video Coding (VVC). It is interesting to find that with an increase in compression distortion, the accuracy of human vision in determining the AIGIs descends. The proposed study is expected to shed light on future research that aims to achieve a good balance between authenticity and visual quality.
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