DevilDiffusion: Embedding Hidden Noise Backdoors into Diffusion Models
Bibliographic record
Abstract
Diffusion models represent state-of-the-art deep learning architectures behind many popular and powerful image-synthesizing generative Artificial Intelligence (AI) systems. Their underlying approach, which relies on scheduled noise addition and optimized noise removal, has been applicable to the syn-thesis of sophisticated and nearly photorealistic images across various domains; however, the potential impacts of compro-mised diffusion models remain an underexplored area in the literature. While prior studies have investigated the insertion of explicit triggers into the noise or prompt spaces of diffusion models, it is unrealistic to assume that benign users would intentionally input such triggers into their own diffusion process. In our DevilDiffusion approach, we demonstrate the capability to surreptitiously embed triggers that leverage uncommon but naturally -occurring characteristics of Gaussian noise directly into the noise space of conditional diffusion models. When these specific characteristics occur in naturally -occurring noise, the model will instead construct our target backdoor image. By adjusting the trigger size and the ratio of poisoned images, we can control the trigger rates of the specified target image, ranging from less than 0.01 % to 25 % of all generated images, while still maintaining performance on the target task.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".