# Radioactive data: tracing through training Source: [Radioactive data: tracing through training]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:03:19.943Z Authors: Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Herv\\'e J\\'egou Published: 2020-02-03T18:41:08.000Z ## Abstract We want to detect whether a particular image dataset has been used to train a model. We propose a new technique, \\emph{radioactive data}, that makes imperceptible changes to this dataset such that any model trained on it will bear an identifiable mark. The mark is robust to strong variations such as different architectures or optimization methods. Given a trained model, our technique detects the use of radioactive data and provides a level of confidence (p-value). Our experiments on large-scale benchmarks (Imagenet), using standard architectures (Resnet-18, VGG-16, Densenet-121) and training procedures, show that we can detect usage of radioactive data with high confidence (p\<10^-4) even when only 1% of the data used to trained our model is radioactive. Our method is robust to data augmentation and the stochasticity of deep network optimization. As a result, it offers a much higher signal-to-noise ratio than data poisoning and backdoor methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dataset Tracking on FairFace](): Dataset Tracking; FairFace; Count A 1 (out of 20): 0. - [Dataset Tracking on CIFAR-10](): Dataset Tracking; CIFAR-10; Component Score A 1: 1. ## Original sources and implementation [Original paper]() [PDF]()