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<p>This study presents a strong framework for the detection of artificial intelligence-generated artwork using digital imaging and deep learning–based transformers models, which helps in art community to distinguish the authenticity of human-created art from highly fascinated machine-generated content. Art holds reflection of deep cultural, historical, and social significance, however, due to rapid advancements in artificial intelligence, particularly in generative adversarial networks and diffusion models, have enabled the production of visually creating artworks that blur the boundaries between originality and synthesis. Traditional methods relying on conventional features and statistical analysis are increasingly lower performance against such advance transformation, highlighting the need for more advanced detection mechanisms. To address this challenge, the proposed approach employs Distillation with No Labels (DINO) v2, a self-distilled transformer model that excels in extracting discriminative features by capturing both global structures and fine-grained visual cues. The model was trained and evaluated on a balanced dataset of real and AI-generated art images, with results benchmarked against strong baselines. Experimental findings demonstrate that the proposed framework achieves 99.01% accuracy, 95.29% precision, 94.58% recall, 94.93% F1-score, and an AUC of 99%, outperforming all baselines with superior generalization. Furthermore, interpretability methods along with statistical validation based on log of p-values, confirmed that predictions are both dependable and transparent.</p>

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AI-generated artwork detection using self-distilled transformers with global–local feature learning and Grad-CAM interpretability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月6日

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  • Wang Yinghua,
  • Li Linyan,
  • Ma Wenjuan,
  • Zhang Yunzhe

摘要

This study presents a strong framework for the detection of artificial intelligence-generated artwork using digital imaging and deep learning–based transformers models, which helps in art community to distinguish the authenticity of human-created art from highly fascinated machine-generated content. Art holds reflection of deep cultural, historical, and social significance, however, due to rapid advancements in artificial intelligence, particularly in generative adversarial networks and diffusion models, have enabled the production of visually creating artworks that blur the boundaries between originality and synthesis. Traditional methods relying on conventional features and statistical analysis are increasingly lower performance against such advance transformation, highlighting the need for more advanced detection mechanisms. To address this challenge, the proposed approach employs Distillation with No Labels (DINO) v2, a self-distilled transformer model that excels in extracting discriminative features by capturing both global structures and fine-grained visual cues. The model was trained and evaluated on a balanced dataset of real and AI-generated art images, with results benchmarked against strong baselines. Experimental findings demonstrate that the proposed framework achieves 99.01% accuracy, 95.29% precision, 94.58% recall, 94.93% F1-score, and an AUC of 99%, outperforming all baselines with superior generalization. Furthermore, interpretability methods along with statistical validation based on log of p-values, confirmed that predictions are both dependable and transparent.