On the Robustness of Backdoor-based Watermarking in Deep Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Watermarking algorithms have been introduced in the past years to protect deep learning models against unauthorized re-distribution. We investigate the robustness and reliability of state-of-the-art deep neural network watermarking schemes. We focus on backdoor-based watermarking and propose two simple yet effective attacks -- a black-box and a white-box -- that remove these watermarks without any labeled data from the ground truth. Our black-box attack steals the model and removes the watermark with only API access to the labels. Our white-box attack proposes an efficient watermark removal when the parameters of the marked model are accessible, and improves the time to steal a model up to twenty times over the time to train a model from scratch. We conclude that these watermarking algorithms are insufficient to defend against redistribution by a motivated attacker.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it