A reversible unlearnable-example framework that pairs output-entropy-minimizing perturbations with a dual watermark extractor, achieving near-random unauthorized accuracy and low watermark BER on three image datasets.
Mbrs: Enhancing robustness of dnn- based watermarking by mini-batch of real and simulated jpeg compres- sion,
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Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era
A reversible unlearnable-example framework that pairs output-entropy-minimizing perturbations with a dual watermark extractor, achieving near-random unauthorized accuracy and low watermark BER on three image datasets.