Problem
Research questions and friction points this paper is trying to address.
AI-generated content
AI labels
security and privacy
traceability
creator perception
Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content
🤖 AI Summary
This study addresses growing concerns among content creators who, misinterpreting binary AI labels as fine-grained tracking tools, often proactively erase digital traces to safeguard privacy and reputation—thereby undermining the traceability and safety mechanisms of AI-generated content (AIGC). Through semi-structured interviews with 21 creators and empirical testing of 16 user-reported image modification techniques across six generative platforms, the research combines qualitative insights with quantitative evaluation of underlying provenance methods, including watermarks and metadata. Findings reveal that defensive de-identification practices—such as coarse quantization—significantly degrade the accuracy of current AI detectors, with substantial inter-platform variability and notable false positives on human-created images. The work thus calls for resilient, implicit AI labeling workflows aligned with creators’ incentive structures.
📝 Abstract
AI labels, typically implemented via underlying tracing mechanisms such as watermarks and metadata, are crucial for protecting Artificial Intelligence-Generated Content (AIGC) against security threats like disinformation and evasion. However, the perceived devaluation of AI-assisted work discourages creators from disclosing AI use, incentivizing efforts to bypass labeling and compromising downstream traceability. Yet, how AIGC creators perceive the security and privacy (S\&P) implications of these labels, and how their behaviors impact technical resilience remain underexplored. To this end, we conducted semi-structured interviews with 21 AIGC creators and measured images across 6 image generation platforms against 16 self-reported manipulation settings. Our findings reveal that creators conflate binary AI labels with granular traceability, and express strong fears of de-anonymization via platform identifiers. Driven by fears of algorithmic traffic suppression and reputational risks, they defensively removed digital traces. Through empirical tests, we show that targeted modifications like coarse quantization significantly degrade detection. AI detection capabilities are also inconsistent across platforms, and suffer from false positives even for human-authored images. Based on these insights, we advocate for workflow-resilient implicit AI labels that align technical guarantees with creators' incentives.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-generated content
AI labeling
security and privacy perceptions
creator behavior
workflow-resilient watermarking
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