arXiv:2606.20155v1 Announce Type: cross Abstract: Text-to-image (T2I) models generate realistic likenesses of some individuals when prompted with their names, raising privacy concerns. However, distinguishing whether a generated face is memorized or fabricated currently requires ground-truth photos, access to training data, or white-box access to model internals, limiting applicability. We introduce a fully black-box behavioral probe that distinguishes between these regimes while requiring no reference photos or prior knowledge of training data. To benchmark this task, we present the NAMESAKES
The proliferation of advanced text-to-image models has made the generation of realistic likenesses a pressing privacy and ethical concern, demanding immediate solutions for identification and mitigation.
This research provides a crucial black-box method for identifying memorization in T2I models, enabling better governance, privacy protection, and potentially influencing the development and deployment of future generative AI systems.
The ability to probe identity memorization without specialized access simplifies the detection of privacy risks and reduces reliance on internal model data or ground-truth photos.
- · Privacy advocates
- · Individuals with public profiles
- · AI ethics researchers
- · Regulatory bodies
- · Developers of un-audited T2I models
- · Malicious actors using T2I for identity fakery
- · Companies with lax data governance for training sets
Companies developing T2I models will be pressured to implement better privacy-preserving techniques in their training and generation processes.
New standards and regulations around 'right to be forgotten' and identity protection in generative AI could emerge, impacting model architectures and data collection.
The development of robust black-box auditing tools may lead to a broader 'accountability AI' industry focused on verifying ethical and legal compliance of AI systems.
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