Fast and Effective Overwrite Attack Against DNN-Based Image Watermarking Models
Bibliographic record
Abstract
Deep neural network (DNN)-based image watermarking models have been widely recognized as an effective way to manage the huge amount of AI-generated images. However, the vulnerability of such models to different forms of adversarial attacks has been a critical concern. Among the existing forms of attacks in the literature, image-dependent attacks cannot launch real-time attacks on a large number of watermarked images, because they need to train a new noise image to attack each new watermarked image; image-regeneration attacks either require a lot of information about the watermarking system or cause too much damage to the attacked image. To fill the gap in the existing forms of attacks, in this paper, we propose a novel form of attack named “fast and effective overwrite attack (FEOA)”, which achieves an extremely fast attack speed and strong attack effectiveness. In particular, we discovered a single noise image, when directly added to many watermarked images, can overwrite their true watermark messages to different ones in milliseconds. We also develop an adaptive version of FEOA, which trains$k$different noise images and applies the principle of divide and conquer to significantly improve attack effectiveness. Our work opens the door to quickly launching massive overwrite attacks on a large number of watermarked images, revealing a new robustness issue of DNN-based image watermarking models. Extensive experiments demonstrate the outstanding attack time efficiency and effectiveness of our methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".