arXiv:2605.30462v1 Announce Type: new Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned semantic correlation structure: incidental regularities that are predictive within a dataset, but not causal for the underlying task, can be internalized during training. We use this insight to study dataset-level membership inference, moving beyond existing methods that rely on behavioral or distributional evidence such as confidence scores, losses, margins, generated samples, or query r
The increasing sophistication and scale of AI models highlight the critical need for methods to understand and attribute their training data, especially as AI governance and ethical concerns grow.
This research provides a novel method for identifying the specific datasets used to train AI models, offering significant implications for data privacy, intellectual property, and model attribution.
The ability to 'fingerprint' training data from a model's learned correlations introduces new capabilities for dataset-level membership inference, moving beyond traditional behavioral or distributional cues.
- · AI ethicists and regulators
- · Data rights organizations
- · Organizations with proprietary datasets
- · Malicious actors misusing training data
- · Models trained on unethically sourced data
- · Open-source models without clear data provenance
New tools will emerge that can reliably identify the specific training datasets incorporated into AI models.
This capability could lead to stricter regulations and improved enforcement mechanisms for data licensing and usage in AI development.
The increased transparency around training data may foster greater trust in AI systems and enable more responsible AI deployment, or conversely, create new attack vectors for intellectual property theft.
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Read at arXiv cs.LG