Visual diffusion models can accidentally become art thieves by memorizing training data.
This paper provides the first comprehensive survey of replication in visual diffusion models - where models memorize and reproduce training data during inference. It systematically categorizes existing research into unveiling, understanding, and mitigating replication while examining real-world impacts across regulation, art, society and healthcare.
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https://arxiv.org/abs/2411.10683
🔍 Original Problem:
Visual diffusion models trained on web data often replicate copyrighted content, private information, and artistic styles from their training data, raising serious ethical and legal concerns.
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⚡ Solution in this Paper:
→ The paper develops a systematic framework analyzing replication through three key lenses: unveiling, understanding, and mitigation
→ For unveiling, it examines methods like prompting, membership inference, similarity retrieval, and watermarking to detect replication
→ For understanding, it analyzes how factors like insufficient training data, image duplication, and model capacity contribute to replication
→ For mitigation, it explores techniques like data deduplication, machine unlearning, and prompt disturbing
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💡 Key Insights:
→ 25.93% of evaluated models exhibit identity confusion issues
→ Creation confusion accounts for 63.13% of observed replication cases
→ Trust in models declines sharply for critical tasks when identity confusion occurs
→ Different types of replication require different detection and mitigation approaches
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📊 Results:
→ Analysis of 27 visual diffusion models revealed significant replication vulnerabilities
→ Survey of 208 users showed trust erosion exceeding 49% when models exhibit identity confusion
→ Pre-trained models and modified transformers showed 30% replication rates
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