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• W4221086183 abstract "In a data-driven world, datasets constitute a significant economic value. Dataset owners who spend time and money to collect and curate the data are incentivized to ensure that their datasets are not used in ways that they did not authorize. When such misuse occurs, dataset owners need technical mechanisms for demonstrating their ownership of the dataset in question. Dataset watermarking provides one approach for ownership demonstration which can, in turn, deter unauthorized use. In this paper, we investigate a recently proposed data provenance method, radioactive data, to assess if it can be used to demonstrate ownership of (image) datasets used to train machine learning (ML) models. The original paper radioactive reported that radioactive data is effective in white-box settings. We show that while this is true for large datasets with many classes, it is not as effective for datasets where the number of classes is low (łeq 30) or the number of samples per class is low (łeq 500). We also show that, counter-intuitively, the black-box verification technique described in radioactive is effective for all datasets used in this paper, even when white-box verification in radioactive is not. Given this observation, we show that the confidence in white-box verification can be improved by using watermarked samples directly during the verification process. We also highlight the need to assess the robustness of radioactive data if it were to be used for ownership demonstration since it is an adversarial setting unlike provenance identification." @default.
• W4221086183 created "2022-04-03" @default.
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• W4221086183 date "2022-04-18" @default.
• W4221086183 modified "2023-09-27" @default.
• W4221086183 title "On the Effectiveness of Dataset Watermarking" @default.
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• W4221086183 doi "https://doi.org/10.1145/3510548.3519376" @default.
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