# Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content arXiv: https://arxiv.org/abs/2608.08129 ยท HTML: https://mlsanity.com/paper/2608.08129/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. - Builds on 28 of its 84 references that are in the map. - First posted on arXiv 8 Aug 2026. ## Placed in - LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/) ## Builds on (most cited first, 28 of 28) - [Provable Robust Watermarking for AI-Generated Text](https://mlsanity.com/paper/2306.17439/) (2023) - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021) - [Invisible Image Watermarks Are Provably Removable Using Generative AI](https://mlsanity.com/paper/2306.01953/) (2023) - [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020) - [WAVES: Benchmarking the Robustness of Image Watermarks](https://mlsanity.com/paper/2401.08573/) (2024) - [Prompt Stealing Attacks Against Text-to-Image Generation Models](https://mlsanity.com/paper/2302.09923/) (2023) - [Provably Robust Multi-bit Watermarking for AI-generated Text](https://mlsanity.com/paper/2401.16820/) (2024) - [PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators](https://mlsanity.com/paper/2304.07361/) (2023) - [SynthID-Image: Image watermarking at internet scale](https://mlsanity.com/paper/2510.09263/) (2025) - [UnMarker: A Universal Attack on Defensive Image Watermarking](https://mlsanity.com/paper/2405.08363/) (2024) - [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024) - ["It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models](https://mlsanity.com/paper/2307.10811/) (2023) - [MEA-Defender: A Robust Watermark against Model Extraction Attack](https://mlsanity.com/paper/2401.15239/) (2024) - [Watermarking Autoregressive Image Generation](https://mlsanity.com/paper/2506.16349/) (2025) - [Structural Watermarking to Deep Neural Networks via Network Channel Pruning](https://mlsanity.com/paper/2107.08688/) (2021) - [Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature](https://mlsanity.com/paper/2507.06000/) (2025) - [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025) - [First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge](https://mlsanity.com/paper/2508.21072/) (2025) - [DeepEclipse: How to Break White-Box DNN-Watermarking Schemes](https://mlsanity.com/paper/2403.03590/) (2024) - [You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information](https://mlsanity.com/paper/1803.10133/) (2018) - ["Confrontation or Acceptance": Understanding Novice Visual Artists' Perception towards AI-assisted Art Creation](https://mlsanity.com/paper/2410.14925/) (2024) - ["That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation](https://mlsanity.com/paper/2505.22845/) (2025) - [Authenticated Contradictions from Desynchronized Provenance and Watermarking](https://mlsanity.com/paper/2603.02378/) (2026) - [Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data](https://mlsanity.com/paper/1901.00897/) (2019) - [Making the Invisible Visible: Risks and Benefits of Disclosing Metadata in Visualization](https://mlsanity.com/paper/2108.13270/) (2021) - [Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain](https://mlsanity.com/paper/2602.02412/) (2026) - [A Systematic Survey of Empirical User Studies of Unintentional Information Disclosure in Everyday Digital Interaction](https://mlsanity.com/paper/2509.16003/) (2025) - [Media Integrity and Authentication: Status, Directions, and Futures](https://mlsanity.com/paper/2602.18681/) (2026) ## Abstract (arXiv) 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.