Low-frequency visual signals embedded in training images can reliably detect if a T2I model was fine-tuned on that dataset, enabling practical dataset-use auditing without degrading image quality or requiring complex per-image optimization.
SpectralCanary is a method for auditing whether text-to-image models were trained on specific datasets by embedding a hidden signal into images before release. It uses a smooth low-frequency mask to blend a canary image into training data, then checks if that signal appears in suspect model outputs. The approach achieves 91-92% detection rates while keeping images visually unchanged.